research articles networking

Research shows networking is painful, but it can be a lot better

Assistant Professor of Organisational Behaviour, Bond Business School, Bond University

Disclosure statement

Libby (Elizabeth) Sander does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Bond University provides funding as a member of The Conversation AU.

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Is it enough to throw a group of people together, give them a name badge and hope for the best? Research suggests it isn’t.

Forming and maintaining strong professional relationships is a key component of career success. These networks help individuals to access resources, information and support. But people often hate networking.

Strong networks provide a range of benefits including learning, sources of information, salary growth , innovation and a means of getting things done. Research shows that people with diverse contacts are able to access information that helps them generate better ideas.

Building professional relationships improves both quality of work and job satisfaction. As the landscape of work changes rapidly, employees are making more frequent career moves, which means that networking is a critical competency .

Networking often doesn’t work

Despite intending to meet new people in networking settings, we often don’t act on these plans. A study of MBA students at a specially organised networking event found that while 95% of attendees wanted to meet new contacts, they spent over half of the time with people they already knew.

For many, the prospect of networking is as appealing as public speaking or a trip to the dentist. In fact, research has shown that networking for the purpose of advancing our professional goals can make us actually feel dirty.

And trying to make new connections isn’t easy. Studies have shown that we tend to gravitate to people we already know, see often, or who are similar to us. This can be challenging for people trying to create new networks. It also explains why some employees feel isolated when trying to join established networks.

A recent study demonstrated that traditional networking in science, technology, engineering and mathematics fields can be gendered. Women make 42% fewer contacts, spend 48% less time talking to them and make 25% fewer LinkedIn connections than their male counterparts.

What networking success looks like

So what can be done to increase the value and success of networking? The benefits of networking are influenced by the dynamics and nature of the network.

Emerging research is highlighting the interaction of the role of trust, the place and space where these events occur, and the role of hosts, to increase the effectiveness of networking. Previous studies show physical proximity to others is important in building new relationships. Employees are often encouraged to relocate to regional economic clusters (Silicon Valley, for example), join incubators and coworking spaces, and find ways to be close to other entrepreneurs, investors and customers.

But for people to form new connections, research indicates that social, not just physical barriers, need to be reduced.

One of the solutions to this are structured events to reduce these barriers and decrease search efforts to find new networks and opportunities. A recent example of this was a chartered flight from Silicon Valley to the Myriad entrepreneurship festival in Brisbane. The idea of the mid-air networking flight was to create opportunities for established entrepreneurs, investors and business leaders to network with emerging entrepreneurs, students and business people.

But for networking to succeed it needs to be more than one-off events. Studies show that individuals who receive organised introductions make a far greater number of new contacts, and make far stronger connections with these contacts, than those who received no introduction.

These findings emphasise the importance of creating opportunities for both employees and entrepreneurs to connect, beyond just bringing them together in a particular setting or event. In my research , I found that the role of a host in business incubators and coworking spaces was critical in helping to identify and create opportunities to form new and diverse networks.

The hosts of these spaces appear to play a pivotal role in a network. They identify not only those who should connect, but also ensure they are at the right stage to take advantage of the introduction. The host is also able to connect people to the right information and resources.

The success of this role appears to rest heavily on the trust embedded in these established networks. The person who is being asked to connect is more open to the meeting, knowing that the host will have determined that the meeting will be worthwhile for both parties.

Entrepreneurs rely on social ties in building new ventures, and employees rely on effective workplace networks to be innovative and perform better. These new studies show that if the physical setting as well as clear strategies to facilitate new connections and networks are right, then networking wouldn’t be such a pain.

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Educational resources and simple solutions for your research journey

Effective Research Networking Tips for Researchers

Networking—a crucial academic research skill for career advancement— can be challenging, particularly for early career researchers. Academic researchers are often confused and frustrated by the concept of networking because it is not straightforward, and they are not sure where to start. Introverted academic researchers often sit by themselves at conferences with drink in hand because they find networking for research impossible. In fact, some studies have found that networking can make people feel dishonest, phoney, and selfish 1 .

Table of Contents

Importance of networking

Nonetheless, networking is a crucial research skill for students and early career researchers, who likely find it the most intimidating. There are several benefits to research networking. It is a great way to start new collaborations, share research, learn about funding opportunities, and connect with journal editors and reviewers 2 . Networking also enables shared learning, the transfer of technology, and the chance to collaborate on projects. Data shows articles based on collaborative research receives more citations due to the diverse range of authors 3 .

Networking tips for researchers

Now that it’s been established that this is a key skill for academics to build relationships and advance their careers, here are some effective networking tips for researchers.

Attend more conferences

This is a no-brainer, but academic conferences present a wonderful opportunity to network with peers working in the same or related fields. Interactions with like-minded peers can provide a new perspective to your research, even providing new direction for future research. Virtual modes are now available for some conferences, which is great for researchers who find in-person meetings challenging and nerve-wracking. Although virtual meetings lack the personal touch of in-person encounters, they are accessible, affordable, and user-friendly. The availability of recorded sessions makes it simple for participants to join the meetings at their convenience and serves as a great resource for future use.

Communicate your research

Researchers can maximize their conference experience by choosing to present their work either orally or through academic posters . The poster or oral presentation gives you a platform to communicate your research and receive feedback from other researchers. It also enables colleagues in the same or related fields to approach you. This could be a good place to start networking since it’s typical for most researchers to feel at ease discussing their work in public and with individuals who appear interested in it. The next time you present your research at a conference, ask questions and engage in a two-way conversation with researchers. This would allow you to build relationships with these researchers and broaden your network.

Don’t lose touch with your network

Building strong networks call for effort and communication, and these two factors are crucial to the networking process. We have a propensity to drift apart, so sometimes, the connections we make at conferences can be brief and fleeting. The best way to strengthen a relationship is to follow people you meet on various social media platforms , share their work, and leave comments on their most recent articles. Most researchers value emails praising their work, and keeping in touch over social media will only help expand your network. As one of the tips for researchers to search for a research job, use your network to your advantage. Let your network know you are looking for job so they will keep you in mind when suitable positions become available.

Build an online presence

Social media has emerged as an essential tool for networking in the modern world. Researchers can create an online presence for themselves by sharing their work on different platforms and using these channels to interact with peers, ask questions, and provide assistance with others’ research-related needs. These online platforms, like Twitter, Facebook, and LinkedIn, frequently offer a casual setting for two-way interactions that lead to idea sharing, forming of partnerships, and learning about the most recent developments in your area of expertise. Remaining active on social media allows you to have conversations with researchers all over the world and establish some enduring professional connections.

Sign up for workshops and webinars

Training workshops are the simplest means to expand your academic network and meet more researchers. Despite being time-consuming, workshops offer researchers enough exposure and time to ask questions in person, discuss your research, and connect with instructors and other attendees in the interactive sessions. Similarly, participating in webinars can be an excellent educational tool that enables you to meet active researchers working in your area of interest. Learning to connect with researchers who have the potential to become collaborators on future projects are among the top skills of a good researcher.

Become a member of academic associations/societies

A common advice for future researchers is to join academic associations and learned societies as you advance in your research career and gain experience in your field. The resources and information available through these groups and societies not only keep you abreast of the latest in your field but also help you learn about related conferences, job openings, and calls for papers. These associations also frequently provide members with added benefits, such as fee waivers, training, publishing, and funding advice and support. However, to leverage these advantages, it’s important to be an active member of the group.

Make a website and write blogs

Regardless of where you are in your career, one of the effective networking tips is to have a personal website. It comes in handy for those in the final year of their PhD and early career researchers looking for career opportunities. The link to your site can be added to your social media platforms, effectively acting as an online resume that is accessible to anyone with interest in your research. You can create blog posts summarizing your research, research interests, and ideas, future goals, and can even have a short biographical statement on the website. Remember to include your email address and phone number on your website so that prospective employers and researchers can contact you.

A final advice for future researchers – networking effectively can boost your output, productivity, and research caliber. Your network can aid in navigating career paths, securing funding, and also providing a sense of belonging by listening to your struggles and creating long-lasting connections. If you are still having trouble, keep in mind this effective networking tip: find common interests to introduce yourself to researchers at events; if not, mention any skills you have that might be useful for working with them in the future. If you are still at a loss for words, simply express gratitude and appreciation for their work and connection. Happy networking!

• Clark, D. If Networking Makes You Feel Dirty, You’re Doing It Wrong. The Wall Street Journal, 2021. https://www.wsj.com/articles/if-networking-makes-you-feel-dirty-youre-doing-it-wrong-11631883600
• George, E. Networking made easy: Top 10 tips for the smart and savvy researcher. Editage Insights, 2020. https://www.editage.com/insights/networking-made-easy-top-10-tips-for-the-smart-and-savvy-researcher
• Puljak, L. & Vari, S. G. Significance of research networking for enhancing collaboration and research productivity. Croat Med J 55 , 181–183 (2014).

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134. Vishrant Tripathi, Nick Jones, Eytan Modiano, Fresh-CSMA: A Distributed Protocol for Minimizing Age of Information, IEEE Journal on Communications and Networks, 2024.

133. Bai Liu, Quang Nguyen, Qingkai Liang, Eytan Modiano, Tracking Drift-Plus-Penalty: Utility Maximization for Partially Observable and Controllable Networks, IEEE/ACM Transactions on Networking, 2024.

132. Xinzhe Fu, Eytan Modiano, Optimal Routing to Parallel Servers with Unknown Utilities – Multi-armed Bandit With Queues, IEEE/ACM Transactions on Networking, January 2022.

131. Bai Liu, Qingkai Liang, Eytan Modiano, Tracking MaxWeight: Optimal Control for Partially Observable and Controllable Networks, IEEE/ACM Transactions on Networking, August 2023.

130. Xinzhe Fu, Eytan Modiano, Joint Learning and Control in Stochastic Queueing Networks with unknown Utilities, Proceedings of the ACM on Measurement and Analysis of Computing Systems, 2023.

129. Vishrant Tripathi, Rajat Talak, Eytan Modiano, Information Freshness in Multi-Hop Wireless Networks, IEEE/ACM Transactions on Networking,” April 2023.

128.  Xinzhe Fu, Eytan Modiano, “ Learning-NUM: Network Utility Maximization with Unknown Utility Functions and Queueing Delay ,”  IEEE/ACM Transactions on Networking,” 2022.

127.  Bai Liu, Qiaomin Xie, Eytan Modiano,  “ RL-QN: A Reinforcement Learning Framework for Optimal Control of Queueing Systems ,”  ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS), 2022.

126. Xinzhe Fu and E. Modiano,  “ Elastic Job Scheduling with Unknown Utility Functions ,” Performance Evaluation, 2021.

125. Bai Liu and E. Modiano, “ Optimal Control for Networks with Unobservable Malicious Nodes ,”  Performance Evaluation, 2021.

124. Vishrant Tripathi, Rajat Talak, Eytan Modiano, “ Age Optimal Information Gathering and Dissemination on Graphs ,”  Transactions on Mobile Computing, April 2021.

123.  Xinyu Wu, Dan Wu, Eytan Modiano, “ Predicting Failure Cascades in Large Scale Power Systems via the Influence Model Framework, ”  IEEE Transactions on Power Systems, 2021.

122.   Roy D. Yates, Yin Sun, D. Richard Brown III, Sanjit K. Kaul, Eytan Modiano and Sennur Ulukus, “ Age of Information: An Introduction and Survey, ”  Journal on Selected Areas in Communications, February 2021.

121.   Jianan Zhang, Abhishek Sinha, Jaime Llorca, Anonia Tulino, Eytan Modiano, “ Optimal Control of Distributed Computing Networks with Mixed-Cast Traffic Flows ,”  IEEE/ACM Transactions on Networking, 2021.

120.   Thomas Stahlbuhk, Brooke Shrader, Eytan Modiano, “ Learning Algorithms for Minimizing Queue Length Regret ,”  IEEE Transactions on Information Theory, 2021.

119.   Thomas Stahlbuhk, Brooke Shrader, Eytan Modiano, “ Throughput Maximization in Uncooperative Spectrum Sharing Networks ,”  IEEE/ACM IEEE/ACM Transactions on Networking, Vol. 28, No. 6, December 2020.

118.   Thomas Stahlbuhk, Brooke Shrader, Eytan Modiano, “ Learning algorithms for scheduling in wireless networks with unknown channel statistics ,” Ad Hoc Networks, Vol. 85, pp. 131-144, 2019.

117.   Rajat Talak, Eytan Modiano, “ Age-Delay Tradeoffs in Queueing Systems ,”  IEEE Transactions on Information Theory, 2021.

116.   Rajat Talak, Sertac Karaman, Eytan Modiano, “ Improving Age of Information in Wireless Networks with Perfect Channel State Information ,”  IEEE/ACM Transactions on Networking, Vol. 28, No. 4, August 2020.

115.   Igor Kadota and Eytan Modiano, “ Minimizing the Age of Information in Wireless Networks with Stochastic Arrivals ,” IEEE Transactions on Mobile Computing, 2020.

114.   Rajat Talak, Sertac Karaman, Eytan Modiano, “ Optimizing Information Freshness in Wireless Networks under General Interference Constraints ,”  IEEE/ACM transactions on Networking, Vol. 28, No. 1, February 2020.

113.   X. Fu and E. Modiano, “ Fundamental Limits of Volume-based Network DoS Attacks ,” Proceedings of the ACM on Measurement and Analysis of Computing Systems, Vol. 3, No. 3, December 2019. 

112.   Rajat Talak, Sertac Karaman, Eytan Modiano, “ Capacity and Delay Scaling for Broadcast Transmission in Highly Mobile Wireless Networks ,” IEEE Transactions on Mobile Computing, 2019.

111.   Abhishek Sinha and Eytan Modiano, “ Throughput-Optimal Broadcast in Wireless Networks with Point-to-Multipoint Transmissions , IEEE Transactions on Mobile Computing, Vol. 19, No. 9, September 2020.

110.   Yu-Pin Hsu, Eytan Modiano, Lingjie Duan, “ Scheduling Algorithms for Minimizing Age of Information in Wireless Broadcast Networks with Random Arrivals ,”  IEEE Transactions on Mobile Computing, Vol. 19, No. 12, December 2020.

109.   Xiaolin Jiang, Hossein S. Ghadikolaei, Gabor Fodor, Eytan Modiano, Zhibo Pang, Michele Zorzi, Carlo Fischione, “ Low-latency Networking: Where Latency Lurks and How to Tame It ,”  Proceedings of the IEEE, 2019.

108.   Jianan Zhang, Edmund Yeh, Eytan Modiano, “ Robustness of Interdependent Random Geometric Networks ,” IEEE Transactions on Network Science and Engineering, Vol. 6, No. 3, July-September 2019.

107.   Qingkai Liang, Hyang-Won Lee, Eytan Modiano, “ Robust Design of Spectrum-Sharing Networks ,” IEEE Transactions on Mobile Computing, Vol. 18, No. 8, August 2019.

106.   A. Sinha, L. Tassiulas, E. Modiano, “ Throughput-Optimal Broadcast in Wireless Networks with Dynamic Topology ,”  IEEE Transactions on Mobile Computing, Vol. 18, No. 5, May 2019.

105. Igor Kadota, Abhishek Sinha, Eytan Modiano, “ Scheduling Algorithms for Optimizing Age of Information in Wireless Networks With Throughput Constraints ,”  IEEE/ACM Transactions on Networking, August 2019.

104.   Igor Kadota, Abhishek Sinha, Rahul Singh, Elif Uysal-Biyikoglu, Eytan Modjano, “ Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks ,” IEEE/ACM Transactions on Networking, Vol. 26, No. 5, October 2018.

103.   Jianan Zhang and Eytan Modiano, “ Connectivity in Interdependent Networks ,”  IEEE/ACM Transactions on Networking, 2018.

102.   Qingkai Liang, Eytan Modiano, “ Minimizing Queue Length Regret Under Adversarial Network Models ,” Proceedings of the ACM on Measurement and Analysis of Computing Systems, Volume 2, Issue 1, April 2018, Article No.: 11, pp 1-32. (same as Sigmetrics 2018).

101.   A. Sinha and E. Modiano, “ Optimal Control for Generalized Network Flow Problems ,”  IEEE/ACM Transactions on Networking, 2018.

100.   Hossein Shokri-Ghadikolaei, Carlo Fischione, Eytan Modiano  “ Interference Model Similarity Index and Its Applications to mmWave Networks ,”  IEEE Transactions on Wireless Communications, 2018.

99.   Matt Johnston, Eytan Modiano, “ Wireless Scheduling with Delayed CSI: When Distributed Outperforms Centralized, ’ IEEE Transactions on Mobile Computing, 2018.

98.   A. Sinha, G. Paschos, E. Modiano, “ Throughput-Optimal Multi-hop Broadcast Algorithms ,” IEEE/ACM Transactions on Networking, 2017.

97.   Nathan Jones, Georgios Paschos, Brooke Shrader, Eytan Modiano, “ An Overlay Architecture for Throughput Optimal Multipath Routing ,” IEEE/ACM Transactions on Networking, 2017.

96.   Greg Kuperman, Eytan Modiano, “ Providing Guaranteed Protection in Multi-Hop Wireless Networks with Interference Constraints ,” IEEE Transactions on Mobile Computing, 2017.

95.   Matt Johnston, Eytan Modiano, Isaac Kesslassy, “ Channel Probing in Opportunistic Communications Systems ,”  IEEE Transactions on Information Theory, November, 2017.

94.   Anurag Rai, Georgios Paschos, Chih-Ping Lee, Eytan Modiano, “ Loop-Free Backpressure Routing Using Link-Reversal Algorithms “, IEEE/ACM Transactions on Networking, October, 2017.

93.   Matt Johnston and Eytan Modiano, “” Controller Placement in Wireless Networks with Delayed CSI ,” IEEE/ACM Transactions on Networking, 2017.

92.   Jianan Zheng, E. Modiano, D. Hay, “ Enhancing Network Robustness via Shielding ,”  IEEE Transactions on Networking, 2017.

91.   M. Markakis, E. Modiano, J.N. Tsitsiklis, “ Delay Analysis of the Max-Weight Policy under Heavy-Tailed Traffic via Fluid Approximations ,” Mathematics of Operations Research, October, 2017.

90.   Qingkai Liang and E. Modiano, “ Survivability in Time-Varying Graphs ,”  IEEE Transactions on Mobile Computing, 2017.

89.   A. Sinha, G. Paschos, C. P. Li, and E. Modiano, “ Throughput-Optimal Multihop Broadcast on Directed Acyclic Wireless Networks ,” IEEE/ACM Transactions on Networking, Vol. 25, No. 1, Feb. 2017.

88.   G. Celik, S. Borst, , P. Whiting , E. Modiano, “ Dynamic Scheduling with Reconfiguration Delays ,”  Queueing Systems, 2016.

87.  G. Paschos, C. P. Li, E. Modiano, K. Choumas, T. Korakis, “ In-network Congestion Control for Multirate Multicast ,”   IEEE/ACM Transactions on Networking,  2016.

86.   H. Seferoglu and E. Modiano, “ TCP-Aware Backpressure Routing and Scheduling ,” IEEE Transactions on Mobile Computing, 2016.

85.   H. Seferoglu and E. Modiano, “ Separation of Routing and Scheduling in Backpressure-Based Wireless Networks ,” IEEE/ACM Transactions on Networking, Vol. 24, No. 3, 2016.

84.   M. Markakis, E. Modiano, J.N. Tsitsiklis, “ Delay Stability of Back-Pressure Policies in the presence of Heavy-Tailed Traffic ,”  IEEE/ACM Transactions on Networking, 2015.

83.   S. Neumayer, E. Modiano,  “ Network Reliability Under Geographically Correlated Line and Disk Failure Models ,” Computer Networks, to appear, 2016.

82.   S. Neumayer, E. Modiano, A. Efrat, “ Geographic Max-Flow and Min-Cut Under a Circular Disk Failure Model ,” Computer Networks, 2015.

81.   Marzieh Parandehgheibi, Hyang-Won Lee, Eytan Modiano, Survivable Path Sets:  A new approach to survivability in multi-layer networks ,”  IEEE Journal on Lightwave Technology, 2015.

80.   G. Kuperman, E. Modiano, A. Narula-Tam, “ Network Protection with Multiple Availability Guarantees ,” Computer Networks, 2015.

79.   G. Kuperman, E. Modiano, A. Narula-Tam, “ Analysis and Algorithms for Partial Protection in Mesh Networks ,” IEEE/OSA Journal of Optical Communications and Networks, 2014.

78.   Krishna Jagannathan, Mihalis Markakis, Eytan Modiano, John Tsitsiklis, “ Throughput Optimal Scheduling over Time-Varying Channels in the presence of Heavy-Tailed Traffic ,” IEEE Transactions on Information Theory, 2014.

77.   Chih-Ping Li and Eytan Modiano, “ Receiver-Based Flow Control for Networks in Overload ,” IEEE/ACM Transactions on Networking, Vol. 23, No. 2, 2015.

76.   Matthew Johnston, Hyang-Won Lee, Eytan Modiano, “ A Robust Optimization Approach to Backup Network Design with Random Failures ,” IEEE/ACM Transactions on Networking, Vol. 23, No. 4, 2015.

75.   Guner Celik and Eytan Modiano, “ Scheduling in Networks with Time-Varying Channels and Reconfiguration Delay ,” IEEE/ACM Transactions on Networking, Vol. 23, No. 1, 2015.

74.   Matt Johnston, H.W. Lee, E. Modiano, “ Robust Network Design for Stochastic Traffic Demands ,” IEEE Journal of Lightwave Technology, 2013.

73.   Mihalis Markakis, Eytan Modiano, John Tsitsiklis, “ Max-Weight Scheduling in Queueing Networks With Heavy-Tailed Traffic, ” IEEE/ACM Transactions on Networking, 2014.

72.   Kayi Lee, Hyang-Won Lee, Eytan Modiano, “ Maximizing Reliability in WDM Networks through Lightpath Routing ,”  IEEE ACM Transactions on Networking, 2014.

71.   Krishna Jaggannathan and Eytan Modiano, “ The Impact of Queue Length Information on Buffer Overflow in Parallel Queues ,”  IEEE transactions on Information Theory, 2013.

70.   Krishna Jagannathan, Ishai Menashe, Gil Zussman, Eytan Modiano, “ Non-cooperative Spectrum Access – The Dedicated vs. Free Spectrum Choice ,” IEEE JSAC, special issue on Economics of Communication Networks & Systems, to appear, 2012.

69.   Guner Celik and Eytan Modiano, “ Dynamic Server Allocation over Time Varying Channels with Switchover Delay ,” IEEE Transactions on Information Theory, to appear, 2012.

68.   Anand Srinivas and Eytan Modiano, “ Joint Node Placement and Assignment for Throughput Optimization in Mobile Backbone Networks ,” IEEE JSAC, special issue on Communications Challenges and Dynamics for Unmanned Autonomous Vehicles, June, 2012.

67.   Guner Celik and Eytan Modiano, “ Controlled Mobility in Stochastic and Dynamic Wireless Networks ,” Queueing Systems, 2012.

66.   Krishna Jagannathan, Shie Mannor, Ishai Menache, Eytan Modiano, “ A State Action Frequency Approach to Throughput Maximization over Uncertain Wireless Channels ,” Internet Mathematics, Vol. 9, Nos. 2–3: 136–160.

65.   Long Le, E. Modiano, N. Shroff, “Optimal Control of Wireless Networks with Finite Buffers ,” IEEE/ACM Transactions on Networking, to appear, 2012.

64.   K. Jagannathan, M. Markakis, E. Modiano, J. Tsitsiklis, “Queue Length Asymptotics for Generalized Max-Weight Scheduling in the presence of Heavy-Tailed Traffic,” IEEE/ACM Transactions on Networking, Vol. 20, No. 4, August 2012.

63.   Kayi Lee, Hyang-Won Lee, Eytan Modiano, “ Reliability in Layered Networks with Random Link Failures, ” IEEE/ACM Transactions on Networking, December 2011.

62.   Krishna Jagannathan, Eytan Modiano, Lizhong Zheng, “ On the Role of Queue Length Information in Network Control ,” IEEE Transactions on Information Theory, September 2011.

61.   Hyang-Won Lee, Long Le, Eytan Modiano, “ Distributed Throughput Maximization in Wireless Networks via Random Power Allocation, ” IEEE Transactions on Mobile Computing, 2011.

60.   Sebastian Neumayer, Gil Zussman, Rueven Cohen, Eytan Modiano, “ Assessing the Vulnerability of the Fiber Infrastructure to Disasters, ” IEEE/ACM Transactions on Networking, December 2011.

59.   Kayi Lee, Eytan Modiano, Hyang-Won Lee, “ Cross Layer Survivability in WDM-based Networks ,” IEEE/ACM Transactions on Networking, August 2011.

58.   Emily Craparo, Jon How, and Eytan Modiano, “Throughput Optimization in Mobile Backbone Networks,” IEEE Transactions on Mobile Computing, April, 2011.

57.   Hyang-Won Lee, Kayi Lee, and Eytan Modiano, “Diverse Routing in Networks with Probabilistic Failures,” IEEE/ACM Transactions on Networking, December, 2010.

56.   Guner Celik, Gil Zussman, Wajahat Khan and Eytan Modiano, “MAC Protocols For Wireless Networks With Multi-packet Reception Cabaility ,” IEEE Transactions on Mobile Computing, February, 2010.

55.   Atilla Eryilmaz, Asuman Ozdaglar, Devavrat Shah, and Eytan Modiano, “Distributed Cross-Layer Algorithms for the Optimal Control of Multi-hop Wireless Networks,” IEEE/ACM Transactions on Networking, April 2010.

54.   Murtaza Zafer and Eytan Modiano, “Minimum Energy Transmission over a Wireless Channel With Deadline and Power Constraints ,” IEEE Transactions on Automatic Control, pp. 2841-2852, December, 2009.

53.   Murtaza Zafer and Eytan Modiano, “A Calculus Approach to Energy-Efficient Data Transmission with Quality of Service Constraints,” IEEE/ACM Transactions on Networking, 2009.

52.   Anand Srinivas, Gil Zussman, and Eytan Modiano, “Construction and Maintenance of Wireless Mobile Backbone Networks,” IEEE/ACM Transactions on Networking, 2009.

51.   Andrew Brzezinski, Gil Zussman, and Eytan Modiano, “Distributed Throughput Maximization in Wireless Mesh Networks Via Pre-Partitioning,” IEEE/ACM Transactions on Networking, December, 2008.

50.   Amir Khandani, Eytan Modiano, Jinane Abounadi, Lizhong Zheng, “Reliability and Route Diversity in Wireless Networks,” IEEE Transactions on Wireless Communications, December, 2008.

49.   Alessandro Tarello, Jun Sun, Murtaza Zafer and Eytan Modiano, “Minimum Energy Transmission Scheduling Subject to Deadline Constraints,” ACM Wireless Networks, October, 2008.

48.   Murtaza Zafer, Eytan Modiano, “Optimal Rate Control for Delay-Constrained Data Transmission over a Wireless Channel,” IEEE Transactions on Information Theory, September, 2008.

47.   Andrew Brzezinski and Eytan Modiano, “Achieving 100% Throughput In Reconfigurable IP/WDM Networks,” IEEE/ACM Transactions on Networking, August, 2008.

46.   Michael Neely, Eytan Modiano and C. Li, “Fairness and Optimal Stochastic Control for Heterogeneous Networks,” IEEE/ACM Transactions on Networking, September, 2008.

45.   Amir Khandani, Jinane Abounadi, Eytan Modiano, Lizhong Zheng, “Cooperative Routing in Static Wireless Networks,” IEEE Transactions on Communications, November 2007.

44.   Murtaza Zafer, Eytan Modiano, “Joint Scheduling of Rate-guaranteed and Best-effort Users over a Wireless Fading Channel,” IEEE Transactions on Wireless Communications, October, 2007.

43.   Krishna Jagannathan, Sem Borst, Phil Whiting and Eytan Modiano, “Scheduling of Multi-Antenna Broadcast Systems with Heterogeneous Users,” IEEE Journal of Selected Areas in Communications, September, 2007.Amir Khandani, Jinane

42.   Anand Ganti, Eytan Modiano, and John Tsitsiklis, “Optimal Transmission Scheduling in Symmetric Communication Models with Intermittent Connectivity, ” IEEE Transactions on Information Theory, March, 2007.

41.   Michael Neely and Eytan Modiano, “Logarithmic Delay for NxN Packet Switches Under Crossbar Constraints,” IEEE/ACM Transactions on Networking, November, 2007.

40.   Jun Sun, Jay Gao, Shervin Shambayati and Eytan Modiano, “Ka-Band Link Optimization with Rate Adaptation for Mars and Lunar Communications,”   International Journal of Satellite Communications and Networks, March, 2007.

39.   Jun Sun and Eytan Modiano, “Fair Allocation of A Wireless Fading Channel: An Auction Approach” Institute for Mathematics and its Applications, Volume 143: Wireless Communications, 2006.

38.   Jun Sun, Eytan Modiano and Lizhong Zhang, “Wireless Channel Allocation Using An Auction Algorithm,” IEEE Journal on Selected Areas in Communications, May, 2006.

37.   Murtaza Zafer and Eytan Modiano, “Blocking Probability and Channel Assignment for Connection Oriented Traffic in Wireless Networks,” IEEE Transactions on Wireless Communications, April, 2006.

36.   Alvin Fu, Eytan Modiano, and John Tsitsiklis, “Optimal Transmission Scheduling over a fading channel with Energy and Deadline Constraints” IEEE Transactions on Wireless Communications, March,2006.

35.   Poompat Saengudomlert, Eytan Modiano and Rober Gallager, “On-line Routing and Wavelength Assignment for Dynamic Traffic in WDM Ring and Torus Networks,” IEEE Transactions on Networking, April, 2006.

34.   Li-Wei Chen, Eytan Modiano and Poompat Saengudomlert, “Uniform vs. Non-Uniform band Switching in WDM Networks,” Computer Networks (special issue on optical networks), January, 2006.

33.   Andrew Brzezinski and Eytan Modiano, “Dynamic Reconfiguration and Routing Algorithms for IP-over-WDM networks with Stochastic Traffic,” IEEE Journal of Lightwave Technology, November, 2005

32.   Randall Berry and Eytan Modiano, “Optimal Transceiver Scheduling in WDM/TDM Networks,” IEEE Journal on Selected Areas in Communications, August, 2005.

31.   Poompat Saengudomlert, Eytan Modiano, and Robert G. Gallager, “Dynamic Wavelength Assignment for WDM All-Optical Tree Networks,” IEEE Transactions on Networking, August, 2005.

30.   Ashwinder Ahluwalia and Eytan Modiano, “On the Complexity and Distributed Construction of Energy Efficient Broadcast Trees in Wireless Ad Hoc Networks,” IEEE Transactions on Wireless Communications, October, 2005.

29.   Michael Neely, Charlie Rohrs and Eytan Modiano, “Equivalent Models for Analysis of Deterministic Service Time Tree Networks,” IEEE Transactions on Information Theory, October, 2005.

28.   Michael Neely and Eytan Modiano, “Capacity and Delay Tradeoffs for Ad Hoc Mobile Networks,” IEEE Transactions on Information Theory, May, 2005.

27.   Li-Wei Chen and Eytan Modiano, “Efficient Routing and Wavelength Assignment for Reconfigurable WDM Networks with Wavelength Converters,” IEEE/ACM Transactions on Networking, February, 2005. Selected as one of the best papers from Infocom 2003 for fast-track publication in IEEE/ACM Transactions on Networking.

26.   Michael Neely and Eytan Modiano, “Convexity in Queues with General Inputs,” IEEE Transactions on Information Theory, May, 2005.

25.   Anand Srinivas and Eytan Modiano, “Finding Minimum Energy Disjoint Paths in Wireless Ad Hoc Networks,” ACM Wireless Networks, November, 2005. Selected to appear in a special issue dedicated to best papers from Mobicom 2003.

24.   Michael Neely, Eytan Modiano and Charlie Rohrs, “Dynamic Power Allocation and Routing for Time-Varying Wireless Networks,” IEEE Journal of Selected Areas in Communication, January, 2005.

23.   Chunmei Liu and Eytan Modiano, “On the performance of additive increase multiplicative decrease (AIMD) protocols in hybrid space-terrestrial networks,” Computer Networks, September, 2004.

22.   Li-Wei Chen and Eytan Modiano, “Dynamic Routing and Wavelength Assignment with Optical Bypass using Ring Embeddings,” Optical Switching and Networking (Elsevier), December, 2004.

21.   Aradhana Narula-Tam, Eytan Modiano and Andrew Brzezinski, “Physical Topology Design for Survivable Routing of Logical Rings in WDM-Based Networks,” IEEE Journal of Selected Areas in Communication, October, 2004.

20.   Randall Berry and Eytan Modiano, “‘The Role of Switching in Reducing the Number of Electronic Ports in WDM Networks,” IEEE Journal of Selected Areas in Communication, October, 2004.

19.   Jun Sun and Eytan Modiano, “Routing Strategies for Maximizing Throughput in LEO Satellite Networks,,” IEEE JSAC, February, 2004.

18.   Jun Sun and Eytan Modiano, “Capacity Provisioning and Failure Recovery for Low Earth Orbit Satellite Networks,” International Journal on Satellite Communications, June, 2003.

17.   Alvin Fu, Eytan Modiano, and John Tsitsiklis, “Optimal Energy Allocation and Admission Control for Communications Satellites,” IEEE Transactions on Networking, June, 2003.

16.   Michael Neely, Eytan Modiano and Charles Rohrs, “Power Allocation and Routing in Multi-Beam Satellites with Time Varying Channels,” IEEE Transactions on Networking, February, 2003.

15.   Eytan Modiano and Aradhana Narula-Tam, “Survivable lightpath routing: a new approach to the design of WDM-based networks,” IEEE Journal of Selected Areas in Communication, May 2002.

14.   Aradhana Narula-Tam, Phil Lin and Eytan Modiano, “Efficient Routing and Wavelength Assignment for Reconfigurable WDM Networks,” IEEE Journal of Selected Areas in Communication, January, 2002.

13.   Brett Schein and Eytan Modiano, “Quantifying the benefits of configurability in circuit-switched WDM ring networks with limited ports per node,” IEEE Journal on Lightwave Technology, June, 2001.

12.   Aradhana Narula-Tam and Eytan Modiano, “Dynamic Load Balancing in WDM Packet Networks with and without Wavelength Constraints,” IEEE Journal of Selected Areas in Communications, October 2000.

11.   Randy Berry and Eytan Modiano, “Reducing Electronic Multiplexing Costs in SONET/WDM Rings with Dynamically Changing Traffic,” IEEE Journal of Selected Areas in Communications, October 2000.

10.   Eytan Modiano and Richard Barry, “A Novel Medium Access Control Protocol for WDM-Based LANs and Access Networks Using a Master-Slave Scheduler,” IEEE Journal on Lightwave Technology, April 2000.

9.   Eytan Modiano and Anthony Ephremides, “Communication Protocols for Secure Distributed Computation of Binary Functions,” Information and Computation, April 2000.

8.   Angela Chiu and Eytan Modiano, “Traffic Grooming Algorithms for Reducing Electronic Multiplexing Costs in WDM Ring Networks,” IEEE Journal on Lightwave Technology, January 2000.

7.   Eytan Modiano, “An Adaptive Algorithm for Optimizing the Packet Size Used in Wireless ARQ Protocols,” Wireless Networks, August 1999.

6.   Eytan Modiano, “Random Algorithms for Scheduling Multicast Traffic in WDM Broadcast-and-Select Networks,” IEEE Transactions on Networking, July, 1999.

5.   Eytan Modiano and Richard Barry, “Architectural Considerations in the Design of WDM-based Optical Access Networks,” Computer Networks, February 1999.

4.   V.W.S. Chan, K. Hall, E. Modiano and K. Rauschenbach, “Architectures and Technologies for High-Speed Optical Data Networks,” IEEE Journal of Lightwave Technology, December 1998.

3.   Eytan Modiano and Anthony Ephremides, “Efficient Algorithms for Performing Packet Broadcasts in a Mesh Network,” IEEE Transactions on Networking, May 1996.

2.   Eytan Modiano, Jeffrey Wieselthier and Anthony Ephremides, “A Simple Analysis of Queueing Delay in a Tree Network of Discrete-Time Queues with Constant Service Times,” IEEE Transactions on Information Theory, February 1996.

1.   Eytan Modiano and Anthony Ephremides, “Communication Complexity of Secure Distributed Computation in the Presence of Noise,” IEEE Transactions on Information Theory, July 1992.

Other Papers

5.  Eytan Modiano, “Satellite Data Networks,” AIAA Journal on Aerospace Computing, Information and Communication, September, 2004.

4.  Eytan Modiano and Phil Lin, “Traffic Grooming in WDM networks,” IEEE Communications Magazine, July, 2001.

3.  Eytan Modiano and Aradhana Narula, “Mechanisms for Providing Optical Bypass in WDM-based Networks,” SPIE Optical Networks, January 2000.

2.  K. Kuznetsov, N. M. Froberg, Eytan Modiano, et. al., “A Next Generation Optical Regional Access Networks,” IEEE Communications Magazine, January, 2000.

1.  Eytan Modiano, “WDM-based Packet Networks,” (Invited Paper) IEEE Communications Magazine, March 1999.

Conference Papers

246. Xinyu Wu, Dan Wu, Eytan Modiano, “ Overload Balancing in Single-Hop Networks With Bounded Buffers ,” IFIP Networking, 2022.

245.  Xinzhe Fu, Eytan Modiano, “ Optimal Routing for Stream Learning Systems ,”  IEEE Infocom, April 2022.

244.  Vishrant Tripathi, Luca Ballotta, Luca Carlone, E. Modiano, “ Computation and Communication Co-Design for Real-Time Monitoring and Control in Multi-Agent Systems ,”  IEEE Wiopt, 2021.

243. Eray Atay, Igor Kadota, E. Modiano, “ Aging Wireless Bandits: Regret Analysis and Order-Optimal Learning Algorithm ,”  IEEE Wiopt 2021.

242. Xinzhe Fu and E. Modiano,  “ Elastic Job Scheduling with Unknown Utility Functions ,” IFIP Performance, Milan, 2021.

241. Bai Liu and E. Modiano, “ Optimal Control for Networks with Unobservable Malicious Nodes ,”  IFIP Performance, Milan, 2021.

240. Bai Liu, Qiaomin Xie,  Eytan Modiano, “ RL-QN:  A Reinforcement Learning Framework for Optimal Control of Queueing Systems ,”  ACM Sigmetrics Workshop on Reinforcement Learning in Networks and Queues (RLNQ), 2021.

239. Xinzhe Fu and E. Modiano,  “ Learning-NUM: Network Utility Maximization with Unknown Utility Functions and Queueing Delay ,  ACM MobiHoc 2021.  

238. Vishrant Tripathi and Eytan Modiano,  “ An Online Learning Approach to Optimizing Time-Varying Costs of AoI ,”  ACM MobiHoc 2021. 

237.   Igor Kadota, Muhammad Shahir Rahman, and Eytan Modiano, “ WiFresh: Age-of-Information from Theory to Implementation ,”  International Conference on Computer Communications and Networks (ICCCN), 2021.

236. Vishrant Tripathi and Eytan Modiano, “ Age Debt: A General Framework For Minimizing Age of Information ,”  IEEE Infocom Workshop on Age-of-Information, 2021.

235. Igor Kadota, Eytan Modiano, “ Age of Information in Random Access Networks with Stochastic Arrivals ,” IEEE Infocom, 2020.

234. Igor Kadota, M. Shahir Rahman, Eytan Modiano, Poster: Age of Information in Wireless Networks: from Theory to Implementation , ACM Mobicom, 2020.

233. Xinyu Wu, Dan Wu, Eytan Modiano, “ An Influence Model Approach to Failure Cascade Prediction in Large Scale Power Systems ,” IEEE American Control Conference, July, 2020.

232. X. Fu and E. Modiano, “ Fundamental Limits of Volume-based Network DoS Attacks ,” Proc. ACM Sigmetrics, Boston, MA, June 2020.

231. Vishrant Tripathi, Eytan Modiano, “ A Whittle Index Approach to Minimizing Functions of Age of Information ,” Allerton Conference on Communication, Control, and Computing, September 2019.

230. Bai Liu, Xiaomin Xie, Eytan Modiano, “ Reinforcement Learning for Optimal Control of Queueing Systems ,” Allerton Conference on Communication, Control, and Computing, September 2019.

229. Rajat Talak, Sertac Karaman, Eytan Modiano, “ A Theory of Uncertainty Variables for State Estimation and Inference ,” Allerton Conference on Communication, Control, and Computing, September 2019.

228. Rajat Talak, Eytan Modiano, “ Age-Delay Tradeoffs in Single Server Systems ,” IEEE International Symposium on Information Theory, Paris, France, July, 2019.

227. Rajat Talak, Sertac Karaman, Eytan Modiano, “ When a Heavy Tailed Service Minimizes Age of Information ,” IEEE International Symposium on Information Theory, Paris, France, July, 2019.

226. Qingkai Liang, Eytan Modiano, “ Optimal Network Control with Adversarial Uncontrollable Nodes ,” ACM MobiHoc, Catania, Italy, June 2019.

225. Igor Kadota, Eytan Modiano, “ Minimizing the Age of Information in Wireless Networks with Stochastic Arrivals ,” ACM MobiHoc, June 2019.

224. Maotong Xu, Jelena Diakonikolas, Suresh Subramaniam, Eytan Modiano, “ A Hierarchical WDM-based Scalable Data Center Network Architecture ,” IEEE International Conference on Communications (ICC), Shanghai, China, June 2019.

223. Maotong Xu, Min Tian, Eytan Modiano, Suresh Subramaniam, “ RHODA Topology Configuration Using Bayesian Optimization

222.   Anurag Rai, Rahul Singh and Eytan Modiano, “ A Distributed Algorithm for Throughput Optimal Routing in Overlay Networks ,”  IFIP Networking 2019, Warsaw, Poland, May 2019.

221.   Qingkai Liang and Eytan Modiano, “ Optimal Network Control in Partially-Controllable Networks ,”  IEEE Infocom, Paris, April 2019.

220.   Xinzhe Fu and Eytan Modiano, “ Network Interdiction Using Adversarial Traffic Flows ,”  IEEE Infocom, Paris, April 2019.

219.   Vishrant Tripathi, Rajat Talak, Eytan Modiano, “ Age Optimal Information Gathering and Dissemination on Graphs ,”  IEEE Infocom, Paris, April 2019.

218.   Jianan Zhang, Hyang-Won Lee, Eytan Modiano, “ On the Robustness of Distributed Computing Networks ,”  DRCN 2019, Coimbra, Portugal, March, 2019.

217.   Hyang-Won Lee, Jianan Zhang and Eytan Modiano, “ Data-driven Localization and Estimation of Disturbance in the Interconnected Power System ,”  IEEE Smartgridcomm, October, 2018.

216.   Jianan Zhang and Eytan Modiano, “ Joint Frequency Regulation and Economic Dispatch Using Limited Communication ,”  IEEE Smartgridcomm, October, 2018.

215.   Rajat Talak, Sertac Karaman, Eytan Modiano, “ Scheduling Policies for Age Minimization in Wireless Networks with Unknown Channel State ,”  IEEE International Symposium on Information Theory, July 2018.

214.   Thomas Stahlbuhk, Brooke Shrader, Eytan Modiano, “ Online Learning Algorithms for Minimizing Queue Length Regret ,”  IEEE International Symposium on Information Theory, July 2018.

213.   Rajat Talak, Sertac Karaman, Eytan Modiano, “ Distributed Scheduling Algorithms for Optimizing Information Freshness in Wireless Networks ,”  IEEE SPAWC, Kalamata, Greece, June, 2018.

212.   Rajat Talak, Sertac Karaman, Eytan Modiano, “ Optimizing Information Freshness in Wireless Networks under General Interference Constraints ,”  ACM MobiHoc 2018, Los Angeles, CA, June 2018.

211.   Thomas Stahlbuhk, Brooke Shrader, Eytan Modiano, “ Learning Algorithms for Scheduling in Wireless Networks with Unknown Channel Statistics ,”  ACM MobiHoc, June 2018.

210.   Khashayar Kamran, Jianan Zhang, Edmund Yeh, Eytan Modiano, “ Robustness of Interdependent Geometric Networks Under Inhomogeneous Failures ,”  Workshop on Spatial Stochastic Models for Wireless Networks (SpaSWiN), Shanghai, China, May 2018.

209.   Rajat Talak, Sertac Karaman, Eytan Modiano, “ Optimizing Age of Information in Wireless Networks with Perfect Channel State Information ,”  Wiopt 2018, Shanghai, China, May 2018.

208.   Abhishek Sinha, Eytan Modiano, “ Network Utility Maximization with Heterogeneous Traffic Flows ,”  Wiopt 2018, Shanghai, China, May 2018.

207.   Qingkai Liang, Eytan Modiano, “ Minimizing Queue Length Regret Under Adversarial Network Models ,”  ACM Sigmetrics, 2018.

206.   Jianan Zhang, Abhishek Sinha, Jaime Llorca, Anonia Tulino, Eytan Modiano, “ Optimal Control of Distributed Computing Networks with Mixed-Cast Traffic Flows ,”  IEEE Infocom, Honolulu, HI, April 2018.

205.   Qingkai Liang, Eytan Modiano, “ Network Utility Maximization in Adversarial Environments ,”  IEEE Infocom, Honolulu, HI, April 2018.

204.   Igor Kadota, Abhishek Sinha, Eytan Modiano, “ Optimizing Age of Information in Wireless Networks with Throughput Constraints ,”  IEEE Infocom, Honolulu, HI, April 2018.

203.   QIngkai Liang, Verina (Fanyu) Que, Eytan Modiano, “ Accelerated Primal-Dual Policy Optimization for Safe Reinforcement Learning ,”  NIPS workshop on “Transparent and interpretable machine learning in safety critical environments,”December 2017.

202.   Rahul Singh, Xueying Guo,Eytan Modiano, “ Risk-Sensitive Optimal Control of Queues ,”  IEEE Conference on Decision and Control (CDC), December 2017.

201.   Rajat Talak, Sertac Karaman, Eytan Modiano, “ Minimizing Age of Information in Multi-Hop Wireless Networks ,”  Allerton Conference on Communication, Control, and Computing, September 2017.

200.   Abhishek Sinha, Eytan Modiano, “ Throughput-Optimal Broadcast in Wireless Networks with Point-to-Multipoint Transmissions ,”  ACM MobiHoc, Madras, India, July 2017.

199.   Rajat Talak, Sertac Karaman, Eytan Modiano, “ Capacity and delay scaling for broadcast transmission in highly mobile wireless networks ,”  ACM MobiHoc, Madras, India, July 2017.

198.5 . Y.-P. Hsu, E. Modiano, and L. Duan, “ Age of Information: Design and Analysis of Optimal Scheduling Algorithms ,”  IEEE International Symposium on Information Theory (ISIT), 2017.

198.   Qingkai Liang and Eytan Modiano, “ Coflow Scheduling in Input-Queued Switches: Optimal Delay Scaling and Algorithms ,”  IEEE Infocom, Atlanta, GA, May 2017.

197.   Jianan Zhang and Eytan Modiano, “ Robust Routing in Interdependent Networks ,”  IEEE Infocom, Atlanta, GA, May 2017.

196.   Abhishek Sinha, Eytan Modiano, “ Optimal Control for Generalized Network Flow Problems ,”  IEEE Infocom, Atlanta, GA, May 2017.

195.   Rajat Talak*, Sertac Karaman, Eytan Modiano, “ Speed Limits in Autonomous Vehicular Networks due to Communication Constraints ,”  IEEE Conference on Decision and Control (CDC), Las Vegas, NV, December 2016.

194.   Marzieh Parandehgheibi*, Konstantin Turitsyn, Eytan Modiano, “ Distributed Frequency Control in Power Grids Under Limited Communication ,”  IEEE Conference on Decision and Control (CDC), Las Vegas, NV, December 2016.

193.   Igor Kadota, Elif Uysal-Biyikoglu, Rahul Singh, Eytan Modiano, “ Minimizing Age of Information in Broadcast Wireless Networks ,”  Allerton Allerton Conference on Communication, Control, and Computing, September 2016.

192.   Jianan Zhang, Edmund Yeh, Eytan Modiano, “ Robustness of Interdependent Random Geometric Networks ,”  Allerton Conference on Communication, Control, and Computing, September 2016.

191.   Abhishek Sinha, Leandros Tassiulas, Eytan Modiano, “ Throughput-Optimal Broadcast in Wireless Networks with Dynamic Topology ,”  ACM MobiHoc’16, Paderborn, Germany, July, 2016. (winner of best paper award)

190.   Abishek Sinha, Georgios Paschos, Eytan Modiano, “ Throughput-Optimal Multi-hop Broadcast Algorithms ,”  ACM MobiHoc’16, Paderborn, Germany, July, 2016.

189.   Thomas Stahlbuhk, Brooke Shrader, Eytan Modiano, “ Throughput Maximization in Uncooperative Spectrum Sharing Networks ,”  IEEE International Symposium on Information Theory, Barcelona, Spain, July 2016.

188.   Thomas Stahlbuhk, Brooke Shrader, Eytan Modiano, “ Topology Control for Wireless Networks with Highly-Directional Antennas ,”  IEEE Wiopt, Tempe, Arizona, May, 2016.

187.   Qingkai Liang, H.W. Lee, Eytan Modiano, “ Robust Design of Spectrum-Sharing Networks ,”  IEEE Wiopt, Tempe, Arizona, May, 2016.

186.   Hossein Shokri-Ghadikolae, Carlo Fischione and Eytan Modiano, “ On the Accuracy of Interference Models in Wireless Communications ,”  IEEE International Conference on Communications (ICC), 2016.

185.   Qingkai Liang and Eytan Modiano, “ Survivability in Time-varying Networks ,”  IEEE Infocom, San Francisco, CA, April 2016.

184.   Kyu S. Kim, Chih-Ping Li, Igor Kadota, Eytan Modiano, “ Optimal Scheduling of Real-Time Traffic in Wireless Networks with Delayed Feedback ,”  Allerton conference on Communication, Control, and Computing, September 2015.

183.   Marzieh Parandehgheibi, Eytan Modiano, “ Modeling the Impact of Communication Loss on the Power Grid Under Emergency Control ,”  IEEE SmartGridComm, Miami, FL, Nov. 2015.

182.   Anurag Rai, Chih-ping Li, Georgios Paschos, Eytan Modiano, “ Loop-Free Backpressure Routing Using Link-Reversal Algorithms ,”  Proceedings of the ACM MobiHoc, July 2015.

181.   Longbo Huang, Eytan Modiano, “ Optimizing Age of Information in a Multiclass Queueing System ,”  Proceedings of IEEE ISIT 2015, Hong Kong, Jun 2015.

180.   M. Johnston, E. Modiano, “ A New Look at Wireless Scheduling with Delayed Information ,”  Proceedings of IEEE ISIT 2015, Hong Kong, June 2015.

179.   M. Johnston, E. Modiano, “ Scheduling over Time Varying Channels with Hidden State Information ,”  Proceedings of IEEE ISIT 2015, Hong Kong, June 2015.

178.   M. Johnston and E. Modiano, “ Controller Placement for Maximum Throughput Under Delayed CSI ,”  IEEE Wiopt, Mombai, India, May 2015.

177.   A. Sinha, G. Paschos, C. P. Li, and E. Modiano, “ Throughput Optimal Broadcast on Directed Acyclic Graphs ,”  IEEE Infocom, Hong Kong, April 2015.

176.   J. Zheng and E. Modiano, “ Enhancing Network Robustness via Shielding ,”  IEEE Design of Reliable Communication Networks, Kansas City, March 2015.

175.   H. W. Lee and E. Modiano, “ Robust Design of Cognitive Radio Networks ,”  Information and Communication Technology Convergence (ICTC), 2014.

174.   Greg Kuperman and Eytan Modiano, “ Disjoint Path Protection in Multi-Hop Wireless Networks with Interference Constraints ,”  IEEE Globecom, Austin, TX, December 2014.

173.   Marzieh Parandehgheibi, Eytan Modiano, David Hay, “ Mitigating Cascading Failures in Interdependent Power Grids and Communication Networks ,”  IEEE Smartgridcomm, Venice, Italy, November 2014.

172.   Georgios Paschos and Eytan Modiano, “ Throughput optimal routing in overlay networks ,”  Allerton conference on Communication, Control, and Computing, September 2014.

171.   Nathan Jones, George Paschos, Brooke Shrader, Eytan Modiano, “ An overlay architecture for Throughput Optimal Multipath Routing ,”  ACM MobiHoc, August 2014.

170.   Matt Johnston, Eytan Modiano, Yuri Polyanskiy, “ Opportunistic Scheduling with Limited Channel State Information: A Rate Distortion Approach ,”  IEEE International Symposium on Information Theory, Honolulu, HI, July 2014.

169.   Chih-Ping Li, Georgios Paschos, Eytan Modiano, Leandros Tassiulas, “ Dynamic Overload Balancing in Server Farms ,”  Networking 2014, Trondheim, Norway, June, 2014.

168.   Hulya Seferonglu and Eytan Modiano, “ TCP-Aware Backpressure Routing and Scheduling ,”  Information Theory and Applications, San Diego, CA, February 2014.

167.   Mihalis Markakis, Eytan Modiano, John Tsitsiklis, “ Delay Stability of Back-Pressure Policies in the presence of Heavy-Tailed Traffic ,”  Information Theory and Applications, San Diego, CA, February 2014.

166.   Kyu Soeb Kim, Chih-ping Li, Eytan Modiano, “ Scheduling Multicast Traffic with Deadlines in Wireless Networks ,”  IEEE Infocom, Toronto, CA, April 2014.

165.   Georgios Paschos, Chih-ping Li, Eytan Modiano, Kostas Choumas, Thanasis Korakis, “ A Demonstration of Multirate Multicast Over an 802.11 Mesh Network ,”  IEEE Infocom, Toronto, CA, April 2014.

164.   Sebastian Neumayer, Eytan Modiano, “ Assessing the Effect of Geographically Correlated Failures on Interconnected Power-Communication Networks ,”  IEEE SmartGridComm, 2013.

163.   Marzieh Parandehgheibi, Eytan Modiano, “ Robustness of Interdependent Networks: The case of communication networks and the power grid ,”  IEEE Globecom, December 2013.

162.   Matt Johnston, Eytan Modiano, “ Optimal Channel Probing in Communication Systems: The Two-Channel Case ,”  IEEE Globecom, December 2013.

161.   Mihalis Markakis, Eytan Modiano, John N. Tsitsiklis, “ Delay Analysis of the Max-Weight Policy under Heavy-Tailed Traffic via Fluid Approximations ,”  Allerton Conference, October 2013.

160.   Matthew Johnston, Isaac Keslassy, Eytan Modiano, “ Channel Probing in Communication Systems: Myopic Policies Are Not Always Optimal ,”  IEEE International Symposium on Information Theory, July 2013.

159.   Krishna P Jagannathan, Libin Jiang, Palthya Lakshma Naik, Eytan Modiano, “ Scheduling Strategies to Mitigate the Impact of Bursty Traffic in Wireless Networks ,”  11th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks Wiopt 2013, Japan, May 2013. (Winner – Best Paper Award).

158.   Hulya Seferoglu and Eytan Modiano, “ Diff-Max: Separation of Routing and Scheduling in Backpressure-Based Wireless Networks ,”  IEEE Infocom, Turin, Italy, April 2013.

157.   Chih-Ping Li, Eytan Modiano, “ Receiver-Based Flow Control for Networks in Overload ,”  IEEE Infocom, Turin, Italy, April 2013.

156.   Nathan Jones, Brooke Shrader, Eytan Modiano, “ Distributed CSMA with Pairwise Coding ,”  IEEE Infocom, Turin, Italy, April 2013.

155.   Greg Kuperman and Eytan Modiano, “ Network Protection with Guaranteed Recovery Times using Recovery Domains ,”  IEEE Infocom, Turin, Italy, April 2013.

154.   Greg Kuperman and Eytan Modiano, “ Providing Protection in Multi-Hop Wireless Networks ,”  IEEE Infocom, Turin, Italy, April 2013.

153.   Greg Kuperman, Eytan Modiano, Aradhana Narula-Tam, “ Network Protection with Multiple Availability Guarantees ,”  IEEE ICC workshop on New Trends in Optical Networks Survivability, June 2012.

152.   Nathaniel Jones, Brooke Shrader, Eytan Modiano, “ Optimal Routing and Scheduling for a Simple Network Coding Scheme ,”  IEEE Infocom, Orlando, Fl, March, 2012.

151.   Mihalis Markakis, Eytan Modiano, John Tsitsiklis, “ Max-Weight Scheduling in Networks with Heavy-Tailed Traffic ,”  IEEE Infocom, Orlando, Fl, March, 2012.

150.   Guner Celik and Eytan Modiano, “ Scheduling in Networks with Time-Varying Channels and Reconfiguration Delay ,”  IEEE Infocom, Orlando, Fl, March, 2012.

149.   Sebastian Neumayer, Alon Efrat, Eytan Modiano, “ Geographic Max-Flow and Min-cut Under a Circular Disk Failure Model ,”  IEEE Infocom (MC), Orlando, Fl, March, 2012.

148.   Marzieh Parandehgheibi, Hyang-Won Lee, and Eytan Modiano, “ Survivable Paths in Multi-Layer Networks ,”  Conference on Information Science and Systems, March, 2012.

147.   Greg Kuperman, Eytan Modiano, and Aradhana Narula-Tam, “ Partial Protection in Networks with Backup Capacity Sharing ,”  Optical Fiber Communications Conference (OFC), Anaheim, CA, March, 2012.

146.   Krishna Jagannathan, Libin Jiang, Eytan Modiano, “ On Scheduling Algorithms Robust to Heavy-Tailed Traffic ,”  Information Theory and Applications (ITA), San Diego, CA, February 2012.

145.   M. Johnston, H.W. Lee, E. Modiano, “ Robust Network Design for Stochastic Traffic Demands ,”  IEEE Globecom, Next Generation Networking Symposium, Houston, TX, December 2011.

144.   S. Neumayer, E. Modiano, “ Network Reliability Under Random Circular Cuts ,”  IEEE Globecom, Optical Networks and Systems Symposium, Houston, TX, December 2011.

143.   H.W. Lee, K. Lee, E. Modiano, “ Maximizing Reliability in WDM Networks through Lightpath Routing ,”  IEEE Globecom, Optical Networks and Systems Symposium, Houston, TX, December 2011.

142.   Guner Celik, Sem Borst, Eytan Modiano, Phil Whiting, “ Variable Frame Based Max-Weight Algorithms for Networks with Switchover Delay ,”  IEEE International Symposium on Information Theory, St. Petersburgh, Russia, August 2011.

141.   Krishna Jaganathan, Ishai Menache, Eytan Modiano, and Gil Zussman, “ Non-cooperative Spectrum Access – The Dedicated vs. Free Spectrum Choice ,”  ACM MOBIHOC’11, May 2011.

140.   Krishna Jagannathan, Shie Mannor, Ishai Menache, Eytan Modiano, “ A State Action Frequency Approach to Throughput Maximization over Uncertain Wireless Channels ,”  IEEE Infocom (Mini-conference), Shanghai, China, April 2011.

139.   Guner Celik, Long B. Le, Eytan Modiano, “ Scheduling in Parallel Queues with Randomly Varying Connectivity and Switchover Delay ,”  IEEE Infocom (Mini-conference), Shanghai, China, April 2011.

138.   Gregory Kuperman, Eytan Modiano, Aradhana Narula-Tam, “ Analysis and Algorithms for Partial Protection in Mesh Networks ,”  IEEE Infocom (Mini-conference), Shanghai, China, April 2011.

137.   Matthew Johnston, Hyang-Won Lee, Eytan Modiano, “ A Robust Optimization Approach to Backup Network Design with Random Failures ,”  IEEE Infocom, Shanghai, China, April 2011.

136.   Krishna Jagannathan, Mihalis Markakis, Eytan Modiano, John Tsitsiklis, “ Queue Length Asymptotics for Generalized Max-Weight Scheduling in the presence of Heavy-Tailed Traffic ,”  IEEE Infocom, Shanghai, China, April 2011.

135.   Guner Celik and Eytan Modiano, “ Dynamic Vehicle Routing for Data Gathering in Wireless Networks ,”  In Proc. IEEE CDC’10, Dec. 2010..***

134.   Long B. Le, Eytan Modiano, Changhee Joo, and Ness B. Shroff, “ Longest-queue-first scheduling under the SINR interference model ,”  ACM MobiHoc, September 2010..***

133.   Krishna Jagannathan, Mihalis Markakis, Eytan Modiano, John Tsitsiklis, “ Throughput Optimal Scheduling in the Presence of Heavy-Tailed Traffic ,”  Allerton Conference on Communication, Control, and Computing, September 2010..**

132.   Delia Ciullo, Guner Celik, Eytan Modiano, “ Minimizing Transmission Energy in Sensor Networks via Trajectory Control ,”  IEEE Wiopt 2010, Avignon, France, June 2010, (10 pages; CD proceedings – page numbers not available).

131.   Sebastian Neumayer and Eytan Modiano, “ Network Reliability with Geographically Correlated Failures ,”  IEEE Infocom 2010, San Diego, CA, March 2010, (9 pages; CD proceedings – page numbers not available).**

130.   Long Le, Eytan Modiano, Ness Shroff, “ Optimal Control of Wireless Networks with Finite Buffers ,”  IEEE Infocom 2010, San Diego, CA, March 2010, (9 pages; CD proceedings – page numbers not available).

129.   Kayi Lee, Hyang-Won Lee, Eytan Modiano, “ Reliability in Layered Network with Random Link Failures ,”  IEEE Infocom 2010, San Diego, CA, March 2010, (9 pages; CD proceedings – page numbers not available).**

128.   Krishna Jagannathan, Eytan Modiano, “ The Impact of Queue length Information on Buffer Overflow in Parallel Queues ,”  Allerton Conference on Communication, Control, and Computing, September 2009, pgs. 1103 -1110 **

127.   Mihalis Markakis, Eytan Modiano, John Tsitsiklis, “ Scheduling Policies for Single-Hop with Heavy-Tailed Traffic ,”  Allerton Conference on Communication, Control, and Computing, September 2009, pgs. 112 – 120..**

126.   Dan Kan, Aradhana Narula-Tam, Eytan Modiano, “ Lightpath Routing and Capacity Assignment for Survivable IP-over-WDM Networks ,”  DRCN 2009, Alexandria, VA October 2009, pgs. 37 -44..**

125.   Mehdi Ansari, Alireza Bayesteh, Eytan Modiano, “ Opportunistic Scheduling in Large Scale Wireless Networks ,”  IEEE International Symposium on Information Theory, Seoul, Korea, June 2009, pgs. 1624 – 1628.

124.   Hyang-Won Lee, Eytan Modiano and Long Bao Le, “ Distributed Throughput Maximization in Wireless Networks via Random Power Allocation ,”  IEEE Wiopt, Seoul, Korea, June 2009. (9 pages; CD proceedings – page numbers not available).

123.   Wajahat Khan, Eytan Modiano, Long Le, “ Autonomous Routing Algorithms for Networks with Wide-Spread Failures ,”  IEEE MILCOM, Boston, MA, October 2009. (6 pages; CD proceedings – page numbers not available).**

122.   Guner Celik and Eytan Modiano, “ Random Access Wireless Networks with Controlled Mobility ,”  IEEE Med-Hoc-Nets, Haifa, Israel, June 2009, pgs. 8 – 14.**

121.   Hyang-Won Lee and Eytan Modiano, “ Diverse Routing in Networks with Probabilistic Failures ,”  IEEE Infocom, April 2009, pgs. 1035 – 1043.

120.   Kayi Lee and Eytan Modiano, “ Cross-layer Survivability in WDM-based Networks ,”  IEEE Infocom, April 2009, pgs. 1017 -1025..**

119.   Krishna Jagannathan, Eytan Modiano, Lizhong Zheng, “ On the Trade-off between Control Rate and Congestion in Single Server Systems ,”  IEEE Infocom, April 2009, pgs. 271 – 279.**

118.   Sebastian Neumayer, Gil Zussman, Rueven Cohen, Eytan Modiano, “ Assessing the Vulnerability of the Fiber Infrastructure to Disasters ,”  IEEE Infocom, April 2009, pgs. 1566 – 1574.**

117.   Long Le, Krishna Jagannathan and Eytan Modiano, “ Delay analysis of max-weight scheduling in wireless ad hoc networks ,”  Conference on Information Science and Systems, Baltimore, MD, March, 2009, pgs. 389 – 394.**

116.   Krishna Jagannathan, Eytan Modiano, Lizhong Zheng, “ Effective Resource Allocation in a Queue: How Much Control is Necessary? ,”  Allerton Conference on Communication, Control, and Computing, September 2008, pgs. 508 – 515.**

115.   Sebastian Neumayer, Gil Zussman, Rueven Cohen, Eytan Modiano, “ Assessing the Impact of Geographically Correlated Network Failures ,”  IEEE MILCOM, November 2008. (6 pages; CD proceedings – page numbers not available).**

114.   Emily Craparo, Jonathan P. How, and Eytan Modiano, “ Simultaneous Placement and Assignment for Exploration in Mobile Backbone Networks ,”  IEEE conference on Decision and Control (CDC), November 2008, pgs. 1696 – 1701 **

113.   Anand Srinivas and Eytan Modiano, “ Joint node placement and assignment for throughput optimization in mobile backbone networks ,”  IEEE INFOCOM’08, pp. 1130 – 1138, Phoenix, AZ, Apr. 2008, pgs. 1130 – 1138.**

112.   Guner Celik, Gil Zussman, Wajahat Khan and Eytan Modiano, “ MAC for Networks with Multipacket Reception Capability and Spatially Distributed Nodes ,”  IEEE INFOCOM’08, Phoenix, AZ, Apr. 2008, pgs. 1436 – 1444.**

111.   Gil Zussman, Andrew Brzezinski, and Eytan Modiano, “ Multihop Local Pooling for Distributed Throughput Maximization in Wireless Networks ,”  IEEE INFOCOM’08, Phoenix, AZ, Apr. 2008, pgs 1139 – 1147.**

110.   Emily Craparo, Jonathan How and Eytan Modiano, “ Optimization of Mobile Backbone Networks: Improved Algorithms and Approximation ,”  IEEE American Control Conference, Seattle, WA, June 2008, pgs. 2016 – 2021.**

109.   Atilla Eryilmaz, Asuman Ozdaglar, Devavrat Shah, Eytan Modiano, “ Imperfect Randomized Algorithms for the Optimal Control of Wireless Networks ,”  Conference on Information Science and Systems, Princeton, NJ, March, 2008, pgs. 932 – 937.

108.   Anand Srinivas and Eytan Modiano, “ Optimal Path Planning for Mobile Backbone Networks ,”  Conference on Information Science and Systems, Princeton, NJ, March, 2008, pgs. 913 – 918.

107.   Kayi Lee and Eytan Modiano, “ Cross-layer Survivability in WDM Networks with Multiple Failures ,”  IEEE Optical Fiber Communications Conference, San Diego, CA February, 2008 (3 pages; CD proceedings – page numbers not available).

106.   Andrew Brzezinski, Gil Zussman and Eytan Modiano, “ Local Pooling Conditions for Joint Routing and Scheduling ,”  Workshop on Information Theory and Applications, pp. 499 – 506, La Jolla, CA, January, 2008, pgs. 499 – 506.

105.   Murtaza Zafer and Eytan Modiano, “ Minimum Energy Transmission over a Wireless Fading Channel with Packet Deadlines ,”  Proceedings of IEEE Conference on Decision and Control (CDC), New Orleans, LA, December, 2007, pgs. 1148 – 1155.**

104.   Atilla Eryilmaz, Asuman Ozdaglar, Eytan Modiano, “ Polynomial Complexity Algorithms for Full Utilization of Multi-hop Wireless Networks ,”  IEEE Infocom, Anchorage, AK, April, 2007, pgs. 499 – 507.

103.   Murtaza Zafer and Eytan Modiano, “ Delay Constrained Energy Efficient Data Transmission over a Wireless Fading Channel ,”  Workshop on Information Theory and Application, University of California, San Diego, CA, February, 2007, pgs. 289 – 298.**

102.   Atilla Eryilmaz, Eytan Modiano, Asuman Ozdaglar, “ Randomized Algorithms for Throughput-Optimality and Fairness in Wireless Networks ,”  Proceedings of IEEE Conference on Decision and Control (CDC), San Diego, CA, December, 2006, pgs. 1936 – 1941.

101.   Anand Srinivas, Gil Zussman, and Eytan Modiano, “ Distributed Mobile Disk Cover – A Building Block for Mobile Backbone Networks ,”  Proc. Allerton Conf. on Communication, Control, and Computing, Allerton, IL, September 2006, (9 pages; CD proceedings – page numbers not available).**

100.   Krishna Jagannathan, Sem Borst, Phil Whiting, Eytan Modiano, “ Scheduling of Multi-Antenna Broadcast Systems with Heterogeneous Users ,”  Allerton Conference on Communication, Control and Computing, Allerton, IL, September 2006, (10 pages; CD proceedings – page numbers not available).**

99.   Andrew Brzezinski, Gil Zussman, and Eytan Modiano, “ Enabling Distributed Throughput Maximization in Wireless Mesh Networks – A Partitioning Approach ,”  Proceedings of ACM MOBICOM’06, Los Angeles, CA, Sep. 2006, (12 pages; CD proceedings – page numbers not available).**

98.   Eytan Modiano, Devavrat Shah, and Gil Zussman, “ Maximizing Throughput in Wireless Networks via Gossiping ,”  Proc. ACM SIGMETRICS / IFIP Performance’06, Saint-Malo, France, June 2006, (12 pages; CD proceedings – page numbers not available). (best paper award)

97.   Anand Srinivas, Gil Zussman, and Eytan Modiano, “ Mobile Backbone Networks – Construction and Maintenance ,”  Proc. ACM MOBIHOC’06, Florence, Italy, May 2006, (12 pages; CD proceedings – page numbers not available).**

96.   Andrew Brzezinski and Eytan Modiano, “ Achieving 100% throughput in reconfigurable optical networks ,”  IEEE INFOCOM 2006 High-Speed Networking Workshop, Barcelona, Spain, April 2006, (5 pages; CD proceedings – page numbers not available).**

95.   Krishna P. Jagannathan, Sem Borst, Phil Whiting, Eytan Modiano, “ Efficient scheduling of multi-user multi-antenna systems ,”  Proceedings of WiOpt 2006, Boston, MA, April 2006, (8 pages; CD proceedings – page numbers not available).**

94.   Andrew Brzezinski and Eytan Modiano, “ Greedy weighted matching for scheduling the input-queued switch ,”  Conference on Information Sciences and Systems (CISS), Princeton, NJ, March 2006, pgs. 1738 – 1743.**

93.   Murtaza Zafer and Eytan Modiano, “ Optimal Adaptive Data Transmission over a Fading Channel with Deadline and Power Constraints ,”  Conference on Information Sciences and Systems (CISS), Princeton, New Jersey, March 2006, pgs. 931 – 937.**

92.   Li-Wei Chen and E. Modiano, “ A Geometric Approach to Capacity Provisioning in WDM Networks with Dynamic Traffic ,”  Conference on Information Science and Systems (CISS), Princeton, NJ, March, 2006, pgs. 1676 – 1683, **

91.   Jun Sun and Eytan Modiano, “ Channel Allocation Using Pricing in Satellite Networks ,”  Conference on Information Science and Systems (CISS), Princeton, NJ, March, 2006, pgs. 182 – 187.**

90.   Jun Sun, Jay Gao, Shervin Shambayatti and Eytan Modiano, “ Ka-Band Link Optimization with Rate Adaptation ,”  IEEE Aerospace Conference, Big Sky, MN, March, 2006. (7 pages; CD proceedings – page numbers not available).

89.   Alessandro Tarello, Eytan Modiano and Jay Gao, “ Energy efficient transmission scheduling over Mars proximity links ,”  IEEE Aerospace Conference, Big Sky, MN, March, 2006. (10 pages; CD proceedings – page numbers not available).

88.   A. Brzezinski and E. Modiano, “ RWA decompositions for optimal throughput in reconfigurable optical networks ,”  INFORMS Telecommunications Conference, Dallas, TX, March 2006 (3 pages; CD proceedings – page numbers not available).**

87.   Li Wei Chen and E. Modiano, “ Geometric Capacity Provisioning for Wavelength Switched WDM Networks ,”  Workshop on Information Theory and Application, University of California, San Diego, CA, February, 2006. (8 pages; CD proceedings – page numbers not available).**

86.   Murtaza Zafer and Eytan Modiano, “ Joint Scheduling of Rate-guaranteed and Best-effort Services over a Wireless Channel ,”  IEEE Conference on Decision and Control, Seville, Spain, December, 2005, pgs. 6022–6027.**

85.   Jun Sun and Eytan Modiano, “ Opportunistic Power Allocation for Fading Channels with Non-cooperative Users and Random Access ,”  IEEE BroadNets – Wireless Networking Symposium, Boston, MA, October, 2005, pgs. 397–405.**

84.   Li Wei Chen and Eytan Modiano, “ Uniform vs. Non-uniform Band Switching in WDM Networks ,”  IEEE BroadNets-Optical Networking Symposium, Boston, MA, October, 2005, pgs. 219– 228.**

83.   Sonia Jain and Eytan Modiano, “ Buffer Management Schemes for Enhanced TCP Performance over Satellite Links ,”  IEEE MILCOM, Atlantic City, NJ, October 2005 (8 pages; CD proceedings – page numbers not available).**

82.   Murtaza Zafer and Eytan Modiano, “ Continuous-time Optimal Rate Control for Delay Constrained Data Transmission ,”  Allerton Conference on Communications, Control and Computing, Allerton, IL, September, 2005 (10 pages; CD proceedings – page numbers not available).**

81.   Alessandro Tarello, Eytan Modiano, Jun Sun, Murtaza Zafer, “ Minimum Energy Transmission Scheduling subject to Deadline Constraints ,”  IEEE Wiopt, Trentino, Italy, April, 2005, pgs. 67–76. (Winner of best student paper award).**

80.   Amir Khandani, Eytan Modiano, Jinane Abounadi, Lizhong Zheng, “ Reliability and Route Diversity in Wireless Networks ,”  Conference on Information Science and System, Baltimore, MD, March, 2005, (8 pages; CD proceedings – page numbers not available).**

79.   Andrew Brzezinski, Iraj Saniee, Indra Widjaja, Eytan Modiano, “ Flow Control and Congestion Management for Distributed Scheduling of Burst Transmissions in Time-Domain Wavelength Interleaved Networks ,”  IEEE/OSA Optical Fiber Conference (OFC), Anaheim, CA, March, 2005, pgs. WC4-1–WC4-3.

78.   Andrew Brzezinski and Eytan Modiano, “ Dynamic Reconfiguration and Routing Algorithms for IP-over-WDM Networks with Stochastic Traffic ,”  IEEE Infocom 2005, Miami, FL, March, 2005, pgs. 6–11.**

77.   Murtaza Zafer and Eytan Modiano, “ A Calculus Approach to Minimum Energy Transmission Policies with Quality of Service Guarantees ,”  IEEE Infocom 2005, Miami, FL, March, 2005, pgs. 548–559.**

76.   Michael Neely and Eytan Modiano, “ Fairness and optimal stochastic control for heterogeneous networks ,”  IEEE Infocom 2005, Miami, FL, March, 2005, pgs. 1723 – 1734.**

75.   Aradhana Narula-Tam, Thomas G. Macdonald, Eytan Modiano, and Leslie Servi, “ A Dynamic Resource Allocation Strategy for Satellite Communications ,”  IEEE MILCOM, Monterey, CA, October, 2004, pgs. 1415 – 1421.

74.   Li-Wei Chen, Poompat Saengudomlert and Eytan Modiano, “ Optimal Waveband Switching in WDM Networks ,”  IEEE International Conference on Communication (ICC), Paris, France, June, 2004, pgs. 1604 – 1608.**

73.   Michael Neely and Eytan Modiano, “ Logarithmic Delay for NxN Packet Switches ,”  IEEE Workshop on High performance Switching and Routing (HPSR 2004), Phoenix, AZ, April, 2004, pgs. 3–9.**

72.   Li-Wei Chen and Eytan Modiano, “ Dynamic Routing and Wavelength Assignment with Optical Bypass using Ring Embeddings ,”  IEEE Workshop on High performance Switching and Routing (HPSR 2004), Phoenix, Az, April, 2004, pgs. 119–125.**

71.   Randall Berry and Eytan Modiano, “ On the Benefits of Tunability in Reducing Electronic Port Counts in WDM/TDM Networks ,”  IEEE Infocom, Hong Kong, March 2004, pgs. 1340–1351.

70.   Andrew Brzezinski and Eytan Modiano, “ A new look at dynamic traffic scheduling in WDM networks with transceiver tuning latency ,”  Informs Telecommunications Conference, Boca Raton, FL, March 2004, pgs. 25–26.**

69.   Chunmei Liu and Eytan Modiano, “ Packet Scheduling with Window Service Constraints ,”  Conference on Information Science and System, Princeton, NJ, March, 2004, pgs. 178–184.**

68.   Jun Sun, Eytan Modiano, and Lizhong Zheng, “ A Novel Auction Algorithm for Fair Allocation of a Wireless Fading Channel ,”  Conference on Information Science and System, Princeton, NJ, March, 2004, pgs. 1377–1383.**

67.   Murtaza Zafer and Eytan Modiano, “ Impact of Interference and Channel Assignment on Blocking Probability in Wireless Networks ,”  Conference on Information Science and System, Princeton, NJ, March, 2004, pgs. 430–436.**

66.   Chunmei Liu and Eytan Modiano, “ An Analysis of TCP over Random Access Satellite Links ,”  IEEE Wireless Communications and Networking Conference (WCNC), Atlanta, GA, February, 2004, pgs. 2033–2040..**

65.   Randall Berry and Eytan Modiano, “ Using tunable optical transceivers for reducing the number of ports in WDM/TDM Networks ,”  IEEE/OSA Optical Fiber Conference (OFC), Los Angeles, CA, February, 2004, pgs. 23–27.

64.   Aradhana Narula-Tam, Eytan Modiano and Andrew Brzezinski, “ Physical Topology Design for Survivable Routiing of Logical Rings in WDM-based Networks ,”  IEEE Globecom, San francisco, CA, December, 2003, pgs. 2552–2557.

63.   Jun Sun, Lizhong Zheng and Eytan Modiano, “ Wireless Channel Allocation Using an Auction Algorithm ,”  Allerton Conference on Communications, Control and Computing, October, 2003, pgs. 1114–1123..**

62.   Amir Khandani, Jinane Abounadi, Eytan Modiano, Lizhong Zhang, “ Cooperative Routing in Wireless Networks ,”  Allerton Conference on Communications, Control and Computing, October, 2003, pgs. 1270–1279.**

61.   Poompat Saengudomlert, Eytan Modiano and Robert Gallager, “ Dynamic Wavelength Assignment for WDM all optical Tree Networks ,”  Allerton Conference on Communications, Control and Computing, October, 2003, 915–924.**

60.   Aradhana Narula-Tam and Eytan Modiano, “ Designing Physical Topologies that Enable Survivable Routing of Logical Rings ,”  IEEE Workshop on Design of Reliable Communication Networks (DRCN), October, 2003, pgs. 379–386.

59.   Anand Srinivas and Eytan Modiano, “ Minimum Energy Disjoint Path Routing in Wireless Ad Hoc Networks ,”  ACM Mobicom, San Diego, Ca, September, 2003, pgs. 122–133.**

58.   Michael Neely and Eytan Modiano, “ Improving Delay in Ad-Hoc Mobile Networks Via Redundant Packet Transfers ,”  Conference on Information Science and System, Baltimore, MD, March, 2003 (6 pages; CD proceedings – page numbers not available).**

57.   Michael Neely, Eytan Modiano and Charles Rohrs, “ Dynamic Power Allocation and Routing for Time Varying Wireless Networks ,”  IEEE Infocom 2003, San Francisco, CA, April, 2003, pgs. 745–755.**

56.   Alvin Fu, Eytan Modiano, and John Tsitsiklis, “ Optimal Energy Allocation for Delay-Constrained Data Transmission over a Time-Varying Channel ,”  IEEE Infocom 2003, San Francisco, CA, April, 2003, pgs. 1095–1105.**

55.   Poompat Saengudomlert, Eytan Modiano and Rober Gallager, “ On-line Routing and Wavelength Assignment for Dynamic Traffic in WDM Ring and Torus Networks ,”  IEEE Infocom 2003, San Francisco, CA, April, 2003, pgs. 1805–1815.**

54.   Li-Wei Chen and Eytan Modiano, “ Efficient Routing and Wavelength Assignment for Reconfigurable WDM Networks with Wavelength Converters ,”  IEEE Infocom 2003, San Francisco, CA, April, 2003, pgs. 1785–1794. Selected as one of the best papers of Infocom 2003 for fast track publication in IEEE/ACM Transactions on Networking.**

53.   Mike Neely, Jun Sun and Eytan Modiano, “ Delay and Complexity Tradeoffs for Dynamic Routing and Power Allocation in a Wireless Network ,”  Allerton Conference on Communication, Control, and Computing, Allerton, Illinois, October, 2002, pgs. 157 –159.**

52.   Anand Ganti, Eytan Modiano and John Tsitsiklis, “ Transmission Scheduling for Multi-Channel Satellite and Wireless Networks ,”  Allerton Conference on Communication, Control, and Computing, Allerton, Illinois, October, 2002, pgs. 1318–1327.**

51.   Poompat Saengudomlert, Eytan Modiano, and Robert G. Gallager, “ Optimal Wavelength Assignment for Uniform All-to-All Traffic in WDM Tree Networks ,”  Allerton Conference on Communication, Control, and Computing, Allerton, Illinois, October, 2002, pgs. 528–537.**

50.   Hungjen Wang, Eytan Modiano and Muriel Medard, “ Partial Path Protection for WDM Networks: End-to-End Recovery Using Local Failure Information ,”  IEEE International Symposium on Computer Communications (ISCC), Taormina, Italy, July 2002, pgs. 719–725.**

49.   Jun Sun and Eytan Modiano, “ Capacity Provisioning and Failure Recovery in Mesh-Torus Networks with Application to Satellite Constellations ,”  IEEE International Symposium on Computer Communications (ISCC), Taormina, Italy, July 2002, pgs. 77–84.**

48.   Alvin Fu, Eytan Modiano, and John Tsitsiklis, “ Optimal Energy Allocation and Admission Control for Communications Satellites ,”  IEEE INFOCOM 2002, New York, June, 2002, pgs. 648–656.**

47.   Michael Neely, Eytan Modiano and Charles Rohrs, “ Power and Server Allocation in a Multi-Beam Satellite with Time Varying Channels ,”  IEEE INFOCOM 2002, New York, June, 2002, pgs. 1451–1460..**

46.   Mike Neely, Eytan Modiano and Charles Rohrs, “ Tradeoffs in Delay Guarantees and Computation Complexity for N x N Packet Switches ,”  Conference on Information Science and Systems, Princeton, NJ, March, 2002, pgs. 136–148.**

45.   Alvin Fu, Eytan Modiano and John Tsitsiklis, “ Transmission Scheduling Over a Fading Channel with Energy and Deadline Constraints ,”  Conference on Information Science and System, Princeton, NJ, March, 2002, pgs. 1018–1023.**

44.   Chunmei Liu and Eytan Modiano, “ On the Interaction of Layered Protocols: The Case of Window Flow Control and ARQ ,”  Conference on Information Science and System, Princeton, NJ, March, 2002, pgs. 118–124.**

43.   Mike Neely, Eytan Modiano and Charles Rohrs, “ Packet Routing over Parallel Time-varying Queues with Application to Satellite and Wireless Networks ,”  Conference on Information Science and System, Princeton, NJ, March, 2002, pgs. 360–366.**

42.   Ahluwalia Ashwinder, Eytan Modiano and Li Shu, “ On the Complexity and Distributed Construction of Energy Efficient Broadcast Trees in Static Ad Hoc Wireless Networks ,”  Conference on Information Science and System, Princeton, NJ, March, 2002, pgs. 807–813.**

41.   Jun Sun and Eytan Modiano, “ Capacity Provisioning and Failure Recovery for Satellite Constellations ,”  Conference on Information Science and System, Princeton, NJ, March, 2002, pgs. 1039–1045.**

40.   Eytan Modiano, Hungjen Wang, and Muriel Medard, “ Partial Path Protection for WDM networks ,”  Informs Telecommunications Conference, Boca Raton, FL, March 2002, pgs. 78–79.**

39.   Poompat Saengudomlert, Eytan H. Modiano, and Robert G. Gallager, “ An On-Line Routing and Wavelength Assignment Algorithm for Dynamic Traffic in a WDM Bidirectional Ring ,”  Joint Conference on Information Sciences (JCIS), Durham, North Carolina, March, 2002, pgs. 1331–1334.**

38.   Randy Berry and Eytan Modiano, “ Switching and Traffic Grooming in WDM Networks ,”  Joint Conference on Information Sciences (JCIS), Durham, North Carolina, March, 2002, pgs. 1340–1343.

37.   Eytan Modiano, Hungjen Wang, and Muriel Medard, “ Using Local Information for WDM Network Protection ,”  Joint Conference on Information Sciences (JCIS), Durham, North Carolina, March, 2002, pgs. 1398–1401.**

36.   Aradhana Narula-Tam and Eytan Modiano, “ Network architectures for supporting survivable WDM rings ,”  IEEE/OSA Optical Fiber Conference (OFC) 2002, Anaheim, CA, March, 2002, pgs. 105–107.

35.   Michael Neely, Eytan Modiano, Charles Rohrs, “ Packet Routing over Parallel Time-Varying Queues with Application to Satellite and Wireless Networks ,”  Allerton Conference on Communication, Control, and Computing, Allerton, Illinois, September, 2001, pgs. 1110-1111.**

34.   Eytan Modiano and Randy Berry, “ The Role of Switching in Reducing Network Port Counts ,”  Allerton Conference on Communication, Control, and Computing, Allerton, Illinois, September, 2001, pgs. 376-385.

33.   Eytan Modiano, “ Resource allocation and congestion control in next generation satellite networks ,”  IEEE Gigabit Networking Workshop (GBN 2001), Anchorage, AK, April 2001, (2 page summary-online proceedings).

32.   Eytan Modiano and Aradhana Narula-Tam, “ Survivable Routing of Logical Topologies in WDM Networks ,”  IEEE Infocom 2001, Anchorage, AK, April 2001, pgs. 348–357.

31.   Michael Neely and Eytan Modiano, “ Convexity and Optimal Load Distribution in Work Conserving */*/1 Queues ,”  IEEE Infocom 2001, Anchorage, AK, April 2001, pgs. 1055–1064.

30.   Eytan Modiano and Randy Berry, “ Using Grooming Cross- Connects to Reduce ADM Costs in Sonet/WDM Ring Networks ,”  IEEE/OSA Optical Fiber Conference (OFC) 2001, Anaheim, CA March 2001, pgs. WL1- WL3.

29.   Eytan Modiano and Aradhana Narula-Tam, “ Designing Survivable Networks Using Effective Rounting and Wavelenght Assignment (RWA) ,”  IEEE/OSA Optical Fiber Conference (OFC) 2001, Anaheim, CA March 2001, pgs. TUG5-1 – TUG5– 3.

28.   Roop Ganguly and Eytan Modiano, “ Distributed Algorithms and Architectures for Optical Flow Switching in WDM networks ,”  IEEE International Symposium on Computer Communications (ISCC 2000), Antibes, France, July 2000, pgs. 134–139.

27.   Aradhana Narula-Tam, Philip J. Lin and Eytan Modiano, “ Wavelength Requirements for Virtual topology Reconfiguration in WDM Ring Networks ,”  IEEE International Conference on Communications (ICC 2000), New Orleans, LA, June 2000, pgs. 1650–1654.

26.   Eytan Modiano, “Optical Flow Switching for the Next Generation Internet,”  IEEE Gigabit Networking Workshop (GBN 2000), Tel-aviv, March 2000 (2 page summary-online proceedings).

25.   Aradhana Narula and Eytan Modiano, “ Dynamic Reconfiguration in WDM Packet Networks with Wavelength Limitations ,”  IEEE/OSA Optical Fiber Conference (OFC) 2000, Baltimore, MD, March, 2000, pgs. 1210–1212.

24.   Brett Schein and Eytan Modiano, “ Quantifying the benefits of configurability in circuit-switched WDM ring networks ,”  IEEE Infocom 2000, Tel Aviv, Israel, April, 2000, pgs.1752–1760..***

23.   Aradhana Narula-Tam and Eytan Modiano, “ Load Balancing Algorithms for WDM-based IP networks ,”  IEEE Infocom 2000, Tel Aviv, Israel, April, 2000, pgs. 1010–1019.

22.   Nan Froberg, M. Kuznetsov, E. Modiano, et. al., “ The NGI ONRAMP test bed: Regional Access WDM technology for the Next Generation Internet ,”  IEEE LEOS ’99, October, 1999, pgs. 230–231.

21.   Randy Berry and Eytan Modiano, “ Minimizing Electronic Multiplexing Costs for Dynamic Traffic in Unidirectional SONET Ring Networks ,”  IEEE International Conference on Communications (ICC ’99), Vancouver, CA, June 1999, pgs. 1724–1730..***

20.   Brett Schein and Eytan Modiano, “Increasing Traffic Capacity in WDM Ring Networks via Topology Reconfiguration,”  Conference on Information Science and Systems, Baltimore, MD, March 1999, pgs. 201 – 206.

19.   Eytan Modiano and Richard Barry, “ Design and Analysis of an Asynchronous WDM Local Area Network Using a Master/Slave Scheduler ,”  IEEE Infocom ’99, New York, NY, March 1999, pgs. 900–907.

18.   Randy Berry and Eytan Modiano, “ Grooming Dynamic Traffic in Unidirectional SONET Ring Networks ,”  IEEE/OSA Optical Fiber Conference (OFC) ’99, San Diego, CA, February 1999, pgs. 71–73.

17.   Angela Chiu and Eytan Modiano, “ Reducing Electronic Multiplexing Costs in Unidirectional SONET/WDM Ring Networks Via Efficient Traffic Grooming ,”  IEEE Globecom ’98, Sydney, Australia, November 1998, pgs. 322–327.

16.   Eytan Modiano, “ Throughput Analysis of Unscheduled Multicast Transmissions in WDM Broadcast-and-Select Networks ,”  IEEE International Symposium on Information Theory, Boston, MA, September 1998, pg. 167.

15.   Eytan Modiano and Angela Chiu, “Traffic Grooming Algorithms for Minimizing Electronic Multiplexing Costs in Unidirectional SONET/WDM Ring Networks,”  Conference on Information Science and Systems, Princeton, NJ, March 1998, 653–658.

14.   Eytan Modiano and Eric Swanson, “ An Architecture for Broadband Internet Services over a WDM-based Optical Access Network ,”  IEEE Gigabit Networking Workshop (GBN ’98), San Francisco, CA, March 1998 (2 page summary-online proceedings).

13.   Eytan Modiano, “ Unscheduled Multicasts in WDM Broadcast-and-Select Networks ,”  IEEE Infocom ’98, San Francisco, CA, March 1998, pgs. 86–93.

12.   Eytan Modiano, Richard Barry and Eric Swanson, “ A Novel Architecture and Medium Access Control (MAC) protocol for WDM Networks ,”  IEEE/OSA Optical Fiber Conference (OFC) ’98, San Jose, CA, February 1998, pgs. 90–91.

11.   Eytan Modiano, “ Scheduling Algorithms for Message Transmission Over a Satellite Broadcast System ,”  IEEE MILCOM 97, Monterey, CA, November 1997, pgs. 628–634.

10.   Eytan Modiano, “ Scheduling Packet Transmissions in A Multi-hop Packet Switched Network Based on Message Length ,”  IEEE International Conference on Computer Communications and Networks (IC3N) Las Vegas, Nevada, September 1997, pgs. 350–357.

9.   Eytan Modiano, “A Simple Algorithm for Optimizing the Packet Size Used in ARQ Protocols Based on Retransmission History,”  Conference on Information Science and Systems, Baltimore, MD, March 1997, pgs. 672–677.

8.   Eytan Modiano, “ A Multi-Channel Random Access Protocol for the CDMA Channel ,”  IEEE PIMRC ’95, Toronto, Canada, September 1995, pgs. 799–803.

7.   Eytan Modiano Jeffrey Wieselthier and Anthony Ephremides, “ A Simple Derivation of Queueing Delay in a Tree Network of Discrete-Time Queues with Deterministic Service Times ,”  IEEE International Symposium on Information Theory, Trondheim, Norway, June 1994, pg. 372.

6.   Eytan Modiano, Jeffrey Wieselthier and Anthony Ephremides, “An Approach for the Analysis of Packet Delay in an Integrated Mobile Radio Network,”  Conference on Information Sciences and Systems, Baltimore, MD, March 1993, pgs. 138-139.

5.   Eytan Modiano and Anthony Ephremides, “ A Method for Delay Analysis of Interacting Queues in Multiple Access Systems ,”  IEEE INFOCOM 1993, San Francisco, CA, March 1993, pgs. 447 – 454.

4.   Eytan Modiano and Anthony Ephremides, “ A Model for the Approximation of Interacting Queues that Arise in Multiple Access Schemes ,”  IEEE International Symposium on Information Theory, San Antonio, TX, January 1993, pg. 324.

3.   Eytan Modiano and Anthony Ephremides, “ Efficient Routing Schemes for Multiple Broadcasts in a Mesh ,”  Conference on Information Sciences and Systems, Princeton, NJ, March 1992, pgs. 929 – 934.

2.   Eytan Modiano and Anthony Ephremides, “ On the Secrecy Complexity of Computing a Binary Function of Non-uniformly Distributed Random Variables ,”  IEEE International Symposium on Information Theory, Budapest, Hungary, June 1991, pg. 213.

1.   Eytan Modiano and Anthony Ephremides, “Communication Complexity of Secure Distributed Computation in the Presence of Noise,”  IEEE International Symposium on Information Theory, San Diego, CA, January 1990, pg. 142.

Book Chapters

• Hyang-Won Lee, Kayi Lee, Eytan Modiano, “ Cross-Layer Survivability ” in Cross-Layer Design in Optical Networks, Springer, 2013.
• Li-Wei Chen and Eytan Modiano, “ Geometric Capacity Provisioning for Wavelength-Switched WDM Networks ,” Chapter in Computer Communications and Networks Series: Algorithms for Next Generation Networks, Springer, 2010.
• Amir Khandani, Eytan Modiano, Lizhong Zhang, Jinane Aboundi, “ Cooperative Routing in Wireless Networks ,” Chapter in Advances in Pervasive Computing and Networking, Kluwer Academic Publishers, 2005.
• Jian-Qiang Hu and Eytan Modiano, “ Traffic Grooming in WDM Networks ,” Chapter in Emerging Optical Network Technologies, Kluwer Academic Publishers, to appear, 2004.
• Eytan Modiano, “ WDM Optical Networks ,” Wiley Encyclopedia of Telecommunications (John Proakis, Editor), 2003.
• Eytan Modiano, “ Optical Access Networks for the Next Generation Internet ,” in Optical WDM Networks: Principles and Practice, Kluwer Academic Prublishers, 2002.
• Eytan Modiano, Richard Barry and Eric Swanson, “ A Novel Architecture and Medium Access Control protocol for WDM Networks ,” Trends in Optics and Photonics Series (TOPS) volume on Optical Networks and Their Applications, 1998.
• Eytan Modiano and Kai-Yeung Siu, “Network Flow and Congestion Control,” Wiley Encyclopedia of Electrical and Electronics Engineering, 1999.

Technical Reports

• Amir Khandani, Eytan Modiano, Jinane Abounadi, Lizhong Zheng, “Reliability and Route Diversity in Wireless Networks, ” MIT LIDS Technical Report number 2634, November, 2004.
• Anand Srinivas and Eytan Modiano, “Minimum Energy Disjoint Path Routing in Wireless Ad Hoc Networks, ” MIT LIDS Technical Report, P-2559, March, 2003.
• Eytan Modiano and Aradhana Narula-Tam, “Survivable lightpath routing: a new approach to the design of WDM-based networks, ” LIDS report 2552, October, 2002.
• Michael Neely, Eytan Modiano and Charles Rohrs, “Packet Routing over Parallel Time-Varying Queues with Application to Satellite and Wireless Networks,” LIDS report 2520, September, 2001.
• Jun Sun and Eytan Modiano, “Capacity Provisioning and Failure Recovery in Mesh-Torus Networks with Application to Satellite Constellations,” LIDS report 2518, September, 2001.
• Hungjen Wang, Eytan Modiano and Muriel Medard, “Partial Path Protection for WDM Networks: End-to-End Recovery Using Local Failure Information, ” LIDS report 2517, Sept. 2001.
• Alvin Fu, Eytan Modiano, and John Tsitsiklis, “Optimal Energy Allocation and Admission Control for Communications Satellites, ” LIDS report 2516, September, 2001.
• Michael Neely, Eytan Modiano and Charles Rohrs, “Power and Server Allocation in a Multi-Beam Satellite with Time Varying Channels, ” LIDS report 2515, September, 2001.
• Eytan Modiano, “Scheduling Algorithms for Message Transmission Over the GBS Satellite Broadcast System, ” Lincoln Laboratory Technical Report Number TR-1035, June 1997.
• Eytan Modiano, “Scheduling Packet Transmissions in A Multi-hop Packet Switched Network Based on Message Length, ” Lincoln Laboratory Technical Report number TR-1036, June, 1997.

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• Volume 14, Issue 5
• Use of social network analysis in health research: a scoping review protocol
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• Eshleen Grewal 1 ,
• Jenny Godley 2 , 3 , 4 ,
• Justine Wheeler 5 ,
• http://orcid.org/0000-0001-9008-2289 Karen L Tang 1 , 3 , 4
• 1 Department of Medicine , University of Calgary , Calgary , Alberta , Canada
• 2 Department of Sociology , University of Calgary , Calgary , Alberta , Canada
• 3 Department of Community Health Sciences , University of Calgary , Calgary , Alberta , Canada
• 4 O’Brien Institute for Public Health , University of Calgary , Calgary , Alberta , Canada
• 5 Libraries and Cultural Resources , University of Calgary , Calgary , Alberta , Canada
• Correspondence to Dr Karen L Tang; klktang{at}ucalgary.ca

Introduction Social networks can affect health beliefs, behaviours and outcomes through various mechanisms, including social support, social influence and information diffusion. Social network analysis (SNA), an approach which emerged from the relational perspective in social theory, has been increasingly used in health research. This paper outlines the protocol for a scoping review of literature that uses social network analytical tools to examine the effects of social connections on individual non-communicable disease and health outcomes.

Methods and analysis This scoping review will be guided by Arksey and O’Malley’s framework for conducting scoping reviews. A search of the electronic databases, Ovid Medline, PsycINFO, EMBASE and CINAHL, will be conducted in April 2024 using terms related to SNA. Two reviewers will independently assess the titles and abstracts, then the full text, of identified studies to determine whether they meet inclusion criteria. Studies that use SNA as a tool to examine the effects of social networks on individual physical health, mental health, well-being, health behaviours, healthcare utilisation, or health-related engagement, knowledge, or trust will be included. Studies examining communicable disease prevention, transmission or outcomes will be excluded. Two reviewers will extract data from the included studies. Data will be presented in tables and figures, along with a narrative synthesis.

Ethics and dissemination This scoping review will synthesise data from articles published in peer-reviewed journals. The results of this review will map the ways in which SNA has been used in non-communicable disease health research. It will identify areas of health research where SNA has been heavily used and where future systematic reviews may be needed, as well as areas of opportunity where SNA remains a lesser-used method in exploring the relationship between social connections and health outcomes.

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This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjopen-2023-078872

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STRENGTHS AND LIMITATIONS OF THIS STUDY

This is a novel scoping review that fills an important gap—how and where social network analysis (SNA) (as a data collection and analytical tool) has been used in health research has not been systematically documented despite its increasing use in the discipline.

The breadth of the scoping review allows for a comprehensive mapping of the use of SNA to examine social connections and non-communicable disease and health outcomes, without limiting to any one population group or setting.

The use of the Arksey and O’Malley framework as well as the Levac et al recommendations to guide our scoping review will ensure that a rigorous and transparent process is undertaken.

Due to the scope of the review and the large volume of anticipated studies, only published articles in the English language will be included.

Introduction

Social connections are known to influence health. 1 People with many supportive social connections tend to be healthier and live longer than people who have fewer supportive social connections, while social isolation, or the absence of supportive social connections, is associated with the deterioration of physical and psychological health, and even death. 2–5 These associations hold even when accounting for socioeconomic status and health practices. 6 Additionally, having a low quantity of supportive social connections is associated with the development or worsening of medical conditions, such as atherosclerosis, hypertension, cardiovascular disease and cancer, potentially through chronic inflammation and changes to autonomic regulation and immune responses. 7–13 Unsupportive social connections can also have adverse effects on health due to emotional stress, which can then lead to poor health habits, psychological distress and negative physiological responses (eg, increased heart rate and blood pressure), all of which are detrimental to health over time. 14 The health of individuals is therefore connected to the people around them. 15

Social networks can influence health via five pathways. 15 16 First, networks can provide social support, to meet the needs of the individual. Dyadic relationships can provide informational, instrumental (ie, aid and assistance with tangible needs), appraisal (ie, help with decision-making) and/or emotional support; this support can be enhanced or hindered by the overall network structure. 17 In addition to the tangible aid and resources that are provided, social support—either perceived or actual—also has direct effects on mental health, well-being and feelings of self-efficacy. 18–20 Social support may also act as a buffer to stress. 16 19 The second pathway by which social networks influence health, and in particular health behaviours such as alcohol and cigarette use, physical activity, food intake patterns and healthcare utilisation, is through social influence. 16 21 That is, the attitudes and behaviour of individuals are guided and altered in response to other network members. 22 23 Social influence is difficult to disentangle from social selection from an empirical standpoint. That is, similarities in behaviour may be due to influences within a network, or alternatively, they may reflect the known phenomenon where individuals tend to form close connections with others who are like them. 22 24 The third pathway is through the promotion of social engagement and participation. Individuals derive a sense of identity, value and meaning through the roles they play (eg, parental roles, community roles, professional roles, etc) in their networks, and the opportunities for participation in social contexts. 16 The fourth pathway by which networks affect health is through transmission of communicable diseases through person-to-person contact. Finally, social networks overlap, resulting in differential access to resources and opportunities (eg, finances, information and jobs). 15 16 An individual’s structural position can result in differential health outcomes, similar to the inequities that stem from differences in social status. 16

There has been an explosion of literature in the area of social networks and health. In their bibliometric analysis, Chapman et al found that the number of studies that examine social networks and health has sextupled since 2000. 25 Similarly, the value of grants and contracts in this topic area, as awarded by the National Science Foundation and the National Institutes of Health, has increased 10-fold. 25 A turning point in the field was the HIV epidemic, where there was an urgent need to better understand its spread. 25 The exponential rise in the number of studies since then that examine social networks and health appears to reflect a widespread understanding that an individual’s health cannot be isolated from his or her social networks and context. There is, however, significant heterogeneity in what aspect of, and how, social networks are being studied. For example, many health research studies use proxies for social connectedness such as marital status or living alone status (as these variables tend to be commonly included in health surveys), without considering the quality of those social connections, and without further exploring the broader social network and their characteristics. 16 26 These proxy measures do little to describe the structure, quantity, quality or characteristics of social connections within which individuals are embedded. Another common approach in health research is to focus on social support measures and their effects on health. Individuals are asked about perceived, or received, social support (for example, through questions that ask about the availability of people who provide emotional support, informational support and/or assistance with daily tasks, with either binary or a Likert scale of responses). 27 28 While important, social support measures do not assess the structure of social networks and represent only one of many different mechanisms by which social networks influence health. 17 23

Social network analysis

Social network analysis (SNA) is a methodological tool, developed in the 1930s by social psychologists, used to study the structure and characteristics of the social networks within which individuals are embedded. 16 29 It has evolved over the past 100 years and has been used by researchers in many social science disciplines to analyse how structures of relationships impact social life. 29 30 SNA has the following key properties 3 30 31 : (1) it relies on empirical relational data (ie, data on actors (nodes) and the connections (ties) between them); (2) it uses mathematical models and graph theory to examine the structure of relationships within which individual actors are embedded; and (3) it models social action at both the group and the individual level arising from the opportunities and constraints determined by the system of relationships. The premise of SNA is that social ties are both drivers and consequences of human behaviour, and are therefore the object of study. 15 16 23 32 Social networks are comprised of nodes, representing the members within a network, connected by ties, representing relations among those individuals. 33 There are two types of SNA: egocentric network analysis and whole network analysis. Egocentric network analysis describes the characteristics of an individual’s (ie, the ‘egos’) personal network, while whole network analysis examines the structure of relationships among all the individuals in a bounded group, such as a school or classroom. 3

In egocentric network analysis, a list of ‘alters’ (ie, nodes) to whom the ego is connected, is obtained through a name generator. Name generator questions ask for a list of alters based on role relations (eg, friends or family), affect (eg, people to whom the ego feels close), interaction (eg, people with whom the ego has been in contact) or exchange (eg, people who provide social and/or financial support). 34 These are followed by name interpreters, where the ego is asked questions about the characteristics of each named alter. 35 Analyses of these data involve constructing measures that describe these egocentric networks. Such measures include network size, network density (ie, how tightly knit the network is), the strength of relationships (ie, the intensity and duration of relationships between ego and alter), network function (ie, the resources and/or support provided through the network) and the diversity of relations within the network (‘heterogeneity’). 23 36 In whole network analysis, the network boundary is determined a priori and network members are known, for example, through membership lists or rosters. 37 Each network member is surveyed, to identify the other network members with whom they are connected and/or affiliated; attributes of each member are obtained through surveying the network members themselves. Variables are constructed at the individual and network levels. Individual-level measures include the number of ties to other network members (‘degree’), types of relationships, and the strength and diversity of relationships. Network-level measures include but are not limited to: density (representing how tightly knit or ‘glued’ together the network is), reciprocity (ie, the proportion of network ties that are reciprocated), isolates (ie, nodes with no ties to other network members), centralisation (or the extent to which the network ties are focused on one node or a set of nodes), cliques and equivalence (ie, sets of nodes that have the same pattern of ties and therefore occupy the same position in the network). 33 38 The constructed measures can then be included in statistical models to explore associations between individual and/or network-level measures, and outcomes. 33 39

Study rationale

In medicine and health research, there has traditionally been a dichotomy between the individual and the context in which the individual is situated—such as in their relationships with others. 40 As such, epidemiology of diseases has historically focused on individual-level traditional risk and protective factors—such as biological markers, genetics, lifestyle and health behaviours, and psychological conditions. 41 While criticisms of this individualistic focus abound, attempts to develop and use different approaches in medicine and research have lagged behind. 42 The use and adoption of methods, like SNA, that frame issues of health and wellness differently, has the potential to offer new insights and solutions to clinical and healthcare delivery problems, 42 by more holistically considering ‘different levels of change’ beyond the individual. 41 We seek to examine the extent to which SNA has transcended the boundaries of its disciplines of origin in the social sciences, into health research. For example, while Chapman et al have clearly shown an explosion of publications at this intersection, 25 it remains unclear whether these studies use SNA tools (which were developed specifically to interrogate the nature and characteristics of social networks), or whether they suffer from the known problem of conflation of constructs like social support, social capital and social integration. 15 43 Many studies that report the impact of ‘social networks’ on health outcomes do not use SNA methods but rather use self-reported network size (without probing the network and its structure), 44 45 social support, 46 marital status 47 48 and/or household members 47 as proxies.

We will therefore undertake a scoping review to map the use of SNA as a data collection and analytical method in health research. More specifically, the scoping review will examine how SNA has been used to study associations across social networks and individual health and well-being (including both physical and psychological health), health knowledge, health engagement, health service use and health behaviours. Scoping reviews are a knowledge synthesis approach that aims to uncover the volume, range, reach and coverage of a body of literature on a specific topic. 49 They differ from systematic reviews, another type of knowledge synthesis, in their objectives. Systematic reviews seek to answer clinical or epidemiological questions and are conducted to fill gaps in knowledge. 50 Systematic reviews are used to establish the effectiveness of an intervention or associations between specific exposures and outcomes. On the other hand, scoping reviews do not seek to provide an answer to a question, but rather, aim to create a map of the existing literature. 49 They are used to provide clarity to the concepts and definitions used in literature, examine the way in which research is conducted in a specific field or on a specific topic, and uncover knowledge gaps. 49 A scoping review, therefore, is well suited as a research method to address our research question, of mapping the ways in which SNA has been used in health research. This scoping review can identify areas (eg, specific populations and specific health outcomes) where there has been a plethora of SNA research warranting future systematic reviews. It can also identify areas within health research where the use of SNA is scarce, highlighting topics, populations or outcomes for future study.

This scoping review will be limited to studies that use SNA in exploring network components and their associations with non-communicable diseases and health and well-being outcomes, for three reasons. The first is feasibility, given the large volume of studies anticipated, based on Chapman et al ’s bibliometric study on this topic. 25 Second, the use of SNA in understanding disease transmission of communicable diseases (such as sexually transmitted infections) is well established; its application to HIV was in fact one of the catalysts, as previously mentioned, to its broader uptake in health research. 25 Third, SNA in health research has shifted from focusing on communicable diseases to focusing on non-communicable diseases and their risk factors; SNA is now being applied much more frequently to the latter conditions than the former ones. 51

Methods and analysis

The scoping review will be informed by the framework developed by Arksey and O’Malley 52 for conducting scoping reviews, as well as the additional recommendations made by Levac et al . 53 Arksey and O’Malley’s framework recommends that the review process be organised into the following five steps: identifying the research question; identifying relevant studies; study selection; charting the data; and collating, summarising and reporting the results. 52 The reporting of this review will adhere to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. 54

Patient and public involvement

No patients will be involved.

Step 1: identifying the research question

A preliminary search of the literature identified a gap related to SNA and how it has been used to study the relationship between social networks and individual well-being and health outcomes. This led to the development of the research question that will guide this scoping review: how have social network analytical tools been used to study the associations between social networks and individual patient health? In this case, SNA is defined as a data analysis technique that uses either an egocentric or whole network analysis approach. For egocentric network analysis, we will include studies that involve peer nomination (ie, use of a name generator) and the collection of one or more characteristics of alters (ie, use of name interpreter(s)).

Step 2: identifying relevant studies

A search strategy will be constructed through consultation with an academic librarian (JW). The main concepts from the research question will be used for a preliminary search in Google Scholar. Additionally, the lead authors will provide the librarian with key studies that will be text-mined for relevant terms. These key studies will include a variety of populations (across different countries and age groups) and health outcomes. 55–58 Key studies will be searched in Ovid MEDLINE for appropriate subject headings. In consultation with team members, the librarian (JW) will construct a pilot search strategy. A title/abstract/keyword search will be conducted in Ovid MEDLINE against the known seed/key studies. Table 1 lists example keywords and terms relating to social networks that will be used, with the full search strategy detailed in online supplemental appendix A .

Supplemental material

• View inline

Search terms relating to social network analysis

Due to a significant number of irrelevant articles surrounding communicable diseases using this search strategy, we will exclude records with these terms in either the title or keyword fields. Table 2 lists the terms related to communicable diseases.

Search terms relating to communicable diseases

Of note, the search strategy will not include terms that relate to health-related outcomes of interest (outside of excluding communicable diseases). Prior literature has shown that the inclusion of outcome concepts in a search strategy reduces the recall and sensitivity of a search strategy. 59 60 This problem is further exacerbated when only generic health terms (for example, ‘morbidity’ or ‘health status’) or specific health terms (eg, specific diseases or conditions such as ‘diabetes mellitus’) are used. 61 Because the objective of this scoping review is to examine and map the use of SNA in health research, the outcomes of interest are very broad, including: physical health and well-being, psychological health and well-being, healthcare engagement, health knowledge, health behaviours, healthcare access and use, disease prevalence and outcomes (spanning every organ system), and mortality. It will be impossible for a search strategy to be sufficiently comprehensive, to capture all possible generic and specific terms relating to this broad range of outcomes. In keeping with recommendations to minimise the number of elements in a search strategy 62 —and in particular outcome elements 63 —our search strategy will entail searching for SNA terms in health databases without specifying health outcomes.

The search strategy will first be created in Medline (Ovid), then translated and adapted for the databases: (1) EMBASE (Ovid), (2) APA PsycInfo (Ovid) and (3) CINAHL (EBSCO). A search will be completed in April 2024. No date filters will be applied to the search. However, animal-only studies will be excluded. The current version of the search strategy including limits and filters, for all databases, is included in online supplemental appendix A .

Step 3: study selection

The criteria that will be used to determine which studies to include are as follows:

Studies that employ SNA as a data collection and/or analysis technique, as defined above. Of note, studies that elicit only the number of friends or other social contacts, without collection of any information about these social contacts, are not considered to be SNA and are therefore not included in the scoping review.

Studies that explore the social networks of individuals in whom the health outcome is measured.

Studies must include the exploration of non-communicable health outcomes. Examples include self-rated health or other global measures of health (including measures of physical health, mental health and well-being), health practices (eg, physical activity, dietary patterns, smoking, alcohol use, substance use), sexual and reproductive health, healthcare-seeking behaviours (eg, medication adherence, acute care use, attachment to a primary care provider), health knowledge, health beliefs, healthcare engagement, non-communicable disease prevalence and mortality.

The criteria that will be used to exclude studies are as follows:

Studies that explore the social networks of organisations or healthcare providers, rather than the social networks of the individual about whom the health outcome is measured or reported.

Studies that describe or use data analysis techniques other than SNA (eg, using proxies for social networks/social support that do not include peer nomination (such as marital status or living alone status), or studies where study participants report the number of social contacts but where no other information about each social contact is collected).

Studies that focus exclusively on online social networks (eg, social media, online forums, online support groups).

Studies related to prevention, transmission or outcomes of communicable diseases.

Non-English studies, for feasibility purposes.

We will not limit studies based on the study population or country in which the SNA is conducted. Studies in paediatric and adult populations will be included. The reasons for excluding SNA studies that focus solely on social media and online networks are twofold. First, we anticipate a very large number of articles, given the broad populations and outcomes of interest, and for feasibility purposes, we have needed to narrow the research objective to in-person and/or offline social networks only. Second, there are likely inherent differences in online and offline social networks. Individuals use health-related social networking sites and online networks primarily for information seeking, connection with others who share a similar lived experience while being able to maintain some emotional distance and interacting with health professionals 64 ; this differs from in-person networks, which individuals go to more for emotional and tangible or instrumental support. Friends met on online networks vary from friends met in person in other important ways. They tend to have less similarity in terms of age, gender and place of residence, 65 and the network ties more commonly arise spontaneously—that is, without common acquaintances or affiliations. 66 The social patterns and interactions among individuals and their online network contacts are also different—with entire relationships built on text-based interactions. 66 Therefore, while online social networks are an important area of study, they appear to be inherently different from the study of offline social networks, and are therefore excluded from this scoping review.

For the first step of the screening process, after removing duplicate articles, two reviewers will independently assess the titles and abstracts of the studies to determine whether they meet the inclusion criteria. Any studies that do not meet the inclusion criteria will be excluded from the review. Studies that either one of the two reviewers feels are potentially relevant will be included in the full-text review, to ensure that no article is prematurely excluded at this stage. During the second step of the screening process, two reviewers will independently review the full texts of the studies to ensure they meet the inclusion criteria. Conflicts will be resolved by third and fourth reviewers with expertise in SNA (JG) and health outcomes (KLT). The number of studies included in each step of the screening process will be reported using the Preferred Reporting Items for Systematic reviews and Meta-Analyses diagram. 67

Step 4: charting the data

A data charting document ( online supplemental appendix B ) will be created to extract data from the studies in the review. This document will include information about the authors, year of publication, study location, study population characteristics, outcomes of interest to this scoping review, and the scales and measures used for each outcome. Data about the social network analytical method will also be extracted, including whether studies used egocentric versus whole networks, the name generator used (in egocentric network studies) or the relationship being explored, the maximum number of peer nominations allowed, the lookback period used, whether (and which) alter attributes were collected, and whether alter-to-alter tie data were collected. Data extraction will be performed by at least one reviewer, with a second reviewer separately checking and confirming the inputted data. Disagreements in data extraction will be resolved through a consensus, and through the input of reviewers with content and methods expertise (KLT, JG).

Step 5: collating, summarising and reporting results

The results of the review will be presented in the form of figures and tables and will include descriptive numerical summaries. The numerical summary will include information about the number of studies included in the review, where the studies were conducted, when they were published and characteristics of the populations, such as the sample sizes and mean age. It will also include characteristics of the SNA conducted in these studies, including the number that are whole network studies versus egocentric network studies, the data sources used and the attributes of the social connections that are collected and analysed. Results will be synthesised in text, as well as through tables and figures.

Ethics and dissemination

This review does not require ethics approval. Data will be extracted from published material. Once the scoping review is complete, an article will be written to convey the findings of this review, and it will be submitted for publication in a peer-reviewed journal. We anticipate the results of this review will map out the ways in which SNA has been used in health research. Specifically, this scoping review will identify areas of potential saturation where SNA has been heavily used, opportunities for future systematic reviews (where there is a large body of primary research studies requiring synthesis) and health research gaps (eg, the health outcomes where SNA has been minimally used). The scoping review will also shed light on characteristics of SNA that have been used (eg, whether egocentric networks vs whole networks are used and in what settings, and whether a broad range of social network characteristics are captured and analysed), which will serve to inform the conduct of future SNA studies in health research.

Ethics statements

Patient consent for publication.

Not applicable.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1
• Data supplement 2

Contributors KLT and JG conceived of the study protocol. KLT, JG, EG and JW developed and revised the study protocol, the search strategy and the inclusion/exclusion criteria. EG and KLT drafted the protocol manuscript, and all authors provided critical revisions.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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Social networks as a tool for evidence-based health education: umbrella review.

1. Introduction

Health promotion and education in times of e-professionalism, 2. materials and methods, 2.1. search strategy, 2.2. inclusion and exclusion criteria, 2.3. outcome measures, 2.4. review selection, 2.5. quality assessment, 2.6. data extraction, 2.7. data synthesis, 3.1. evaluation of methodological quality, 3.2. characteristics of the included studies, 3.3. summary of evidence, 3.4. social networking sites most used for health education, 3.4.1. youtube, 3.4.2. x (formerly twitter), 3.4.3. facebook, 3.4.4. instagram, 3.4.5. social networks specific, forums, groups, and blogs, 3.5. target population of health education in social networking sites, 3.6. thematic areas addressed by social networking sites media for health education, 4. discussion, 5. limitations, 6. implications for nursing practice, research, and education, 7. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, public involvement statement, guidelines and standards statement, use of artificial intelligence, conflicts of interest.

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Sufrate-Sorzano, T.; Corton-Carrasco, O.; Garrote-Cámara, M.-E.; Navas-Echazarreta, N.; Pozo-Herce, P.d.; Di Nitto, M.; Juárez-Vela, R.; Santolalla-Arnedo, I. Social Networks as a Tool for Evidence-Based Health Education: Umbrella Review. Nurs. Rep. 2024 , 14 , 2266-2282. https://doi.org/10.3390/nursrep14030168

Sufrate-Sorzano T, Corton-Carrasco O, Garrote-Cámara M-E, Navas-Echazarreta N, Pozo-Herce Pd, Di Nitto M, Juárez-Vela R, Santolalla-Arnedo I. Social Networks as a Tool for Evidence-Based Health Education: Umbrella Review. Nursing Reports . 2024; 14(3):2266-2282. https://doi.org/10.3390/nursrep14030168

Sufrate-Sorzano, Teresa, Olatz Corton-Carrasco, María-Elena Garrote-Cámara, Noelia Navas-Echazarreta, Pablo del Pozo-Herce, Marco Di Nitto, Raúl Juárez-Vela, and Iván Santolalla-Arnedo. 2024. "Social Networks as a Tool for Evidence-Based Health Education: Umbrella Review" Nursing Reports 14, no. 3: 2266-2282. https://doi.org/10.3390/nursrep14030168

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Silver ceiling: Career expert warns delayed retirement trend could have 'ripple effect' on younger generations

Study referenced by axios found 33% of workers plan to retire at age 70 or older or even never.

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FOX Business' Gerri Willis reports on a new Federal Reserve study which found more than 70% of the nation's wealth is owned by those over the age of 55.

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One recent LiveCareer survey, for instance, found that 8 in 10 respondents have considered postponing their retirement, with another 92% concerned they will have no choice but to work longer than planned.

On a similar note, 61% said they fear retirement more than they fear death and 64% fear it more than divorce, with the possibility of running out of money being a driving factor behind that fear.

GEN-Z EMPLOYEE IS ‘SHOCKED’ BY THE ‘DEPRESSING’ 9-TO-5 WORK SCHEDULE

Younger workers could experience a "ripple effect" from older employees' reluctance to retire, a career expert warned.  (Stefan Wermuth/Bloomberg via Getty Images / Getty Images)

Financial troubles are commonplace across the board, however. While older workers fear retirement and running out of savings, younger workers have struggled to experience traditional rites of passage such as owning a home or starting a family due to high living costs.

Combining that cumbersome cost with stagnant wages is a recipe for disaster.

"If you're not moving up in the corporate ladder because there's no space for you to move, then your earning potential is actually stalled," career expert Jasmine Escalera told Axios.

"We may end up seeing a ripple effect where younger generations have a hard time increasing their earning potential, which could potentially also impact their ability to retire at a certain age as well," she added.

BEST AND WORST STATES TO RETIRE IN AMERICA, ACCORDING TO A NEW REPORT

Older adults are worried about retirement funds running out

A recent AARP survey finds over half of adults over 50 are worried about having enough money for retirement.

At the same time, Gary Officer, CEO of the Center for Workforce Inclusion, told the outlet that increased technological demands for higher-end jobs tip the scales in favor of younger, typically more tech-savvy employees.

"Most of the in-demand occupations in this country [require] a higher level of technological proficiency that skews overwhelmingly towards younger people," he said. 

Others say technological skills give younger workers an edge in some career fields, including Taylor Blake from employee learning platform Degreed, who previously shared tips with Fox News Digital to keep older employees on pace with their younger colleagues in the ever-expanding need for technological knowledge like A.I. 

Is Gen Z off to a good start investing for retirement?

 'Barron's Roundtable' discusses reports that Gen Z members are aggressive about wanting to retire.

GET FOX BUSINESS ON THE GO BY CLICKING HERE

FOX Business' Megan Henney contributed to this report.

• Introduction
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eAppendix. Technical appendix

Data Sharing Statement

• Skepticism Calibration Disorder—The New Public Health Crisis JAMA Network Open Invited Commentary September 4, 2024 David A. Asch, MD

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Redelmeier DA , Shafir E. Post Hoc Bias in Treatment Decisions. JAMA Netw Open. 2024;7(9):e2431123. doi:10.1001/jamanetworkopen.2024.31123

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Post Hoc Bias in Treatment Decisions

• 1 Department of Medicine, University of Toronto, Toronto, Ontario, Canada
• 2 Evaluative Clinical Sciences Program, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
• 3 Institute for Clinical Evaluative Sciences, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
• 4 Division of General Internal Medicine, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
• 5 Center for Leading Injury Prevention Practice Education & Research, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
• 6 Department of Psychology, Princeton University, Princeton, New Jersey
• 7 Princeton School of Public and International Affairs, Princeton, New Jersey
• Invited Commentary Skepticism Calibration Disorder—The New Public Health Crisis David A. Asch, MD JAMA Network Open

Question   Do judgments about health treatments show a post hoc bias?

Findings   In this survey study using a series of randomized scenarios, among 1497 patients and 100 clinicians, participants were more likely to continue (or recommend continuing) a dubious treatment when initial care was followed by a marginal improvement in symptoms, despite the change being a potential coincidence.

Meaning   These findings suggest that intuitive reasoning in health care is prone to the post hoc bias, highlighting the importance of clinicians cautioning patients to avoid this bias.

Importance   A goal of health care is to reduce symptoms and improve health status, whereas continuing dubious treatments can contribute to complacency, discourage the search for alternatives, and lead to shortfalls in care.

Objective   To test a potential bias in intuitive reasoning following a marginal improvement in symptoms after a dubious treatment (post hoc bias).

Design, Setting, and Participants   Surveys eliciting treatment recommendations for hypothetical patients were sent to community members throughout North America recruited via an online survey platform in the early winter months of 2023 and 2024 and presented to health care professionals (pharmacists who were approached in person using a secret shopper technique) in the summer months of 2023.

Exposure   Respondents received randomized versions of surveys that differed according to whether vague symptoms improved or remained unchanged after a dubious treatment.

Main Outcomes and Measures   The primary outcome was a recommendation to continue treatment.

Results   In total, 1497 community members (mean [SD] age, 38.1 [12.5] years; 663 female [55.3%]) and 100 health care professionals were contacted. The first scenario described a patient with a sore throat who took unprescribed antibiotics; respondents were more likely to continue antibiotics after initial treatment if there was a marginal improvement in symptoms vs when symptoms remained unchanged (67 of 150 respondents [45%] vs 25 of respondents [17%]; odds ratio [OR], 3.98 [95% CI, 2.33-6.78]; P  < .001). Another scenario described a patient with wrist pain who wore a copper bracelet; respondents were more likely to continue wearing the copper bracelet after initial care was followed by a marginal improvement in symptoms vs when symptoms remained unchanged (78 of 100 respondents [78%] vs 25 of 99 respondents [25%]; OR, 16.19 [95% CI, 5.32-19.52]; P  < .001). A third scenario described a patient with fatigue who took unprescribed vitamin B 12 ; respondents were more likely to continue taking vitamin B 12 when initial treatment was followed by a marginal improvement in symptoms vs when symptoms remained unchanged (80 of 100 respondents [80%] vs 33 of 100 respondents [33%]; OR, 7.91 [95% CI, 4.18-14.97]; P  < .001). Four further scenarios involving dubious treatments found similar results, including when tested on health care professionals.

Conclusions and Relevance   In this study of clinical scenarios, a marginal improvement in symptoms led patients to continue a dubious and sometimes costly treatment, suggesting that clinicians should caution patients against post hoc bias.

The post hoc bias (also termed post hoc ergo propter hoc fallacy ) has been recognized for centuries with enduring relevance. The general implication for medical care is that a patient who improved after a treatment did not necessarily improve as a result of the treatment. 1 Correctly interpreting a marginal improvement in symptoms requires considering alternative explanations, such as withdrawal from adverse activity, added rest, physiologic homeostasis, adaptive immunity, the placebo effect, simple chance, or other confounders. 2 Attributing an improvement to a treatment, however, is reassuring to patients, easily explained to families, and readily justified to others. 3 Attributing an improvement to a treatment is also quick, common, intuitive, and gratifying.

Modern medical care involves fundamental uncertainty because disease mechanisms and treatment pathways are incompletely understood. The uncertainty also contributes to a trial-and-error style of reasoning in practice. The result is that patients and clinicians encounter abundant opportunities for post hoc bias when following up on treatment. 4 For example, vitamin B 12 supplements are sometimes started for vague symptoms, yet improvements are often marginal and some patients are misdiagnosed, leading to delayed diagnosis of a serious underlying disease that subsequently turns incurable. 5 The uncertainty can also contribute to premature closure, polypharmacy, high costs, practice pattern variations, overtreatment, and ultimate disappointment. 6

A tendency toward post hoc bias is similar to tunnel vision or status quo reasoning and can contribute to medical error. 7 Despite the pitfalls of biased post hoc reasoning, however, direct tests of this rudimentary error in intuitive judgment are missing in evidence-based medicine and are rarely the topic of psychological research (beyond lectures or editorials). 8 , 9 Here, we present a series of randomized surveys to test post hoc bias across diverse clinical cases that require subjective judgments. The hypothesis is that people are more likely to continue a treatment, however dubious, when initial care is followed by a marginal symptomatic improvement. Here, we test whether intuitive judgments about health treatments are prone to this bias.

For this survey study, we developed materials using methods adapted from large-scale behavioral decision science. 10 This involved creating different case scenarios describing an individual patient with vague symptoms of ambiguous severity and additional relevant clinical nuances. 11 The purpose in each separate clinical case was to elicit a treatment decision for the hypothetical patient. The question wording was, “Would you recommend that [patient] continue or discontinue [treatment]?” The 4 response options were “definitely continue,” “tend to continue,” “tend to discontinue,” and “definitely discontinue.”

Each scenario appeared in 1 of 2 different versions, termed the improved version and the unchanged version. The improved version described a marginal improvement in symptoms after initial treatment. The unchanged version described unchanged symptoms after initial treatment. No scenario described a worsening of symptoms that might have otherwise justified discontinuing treatment. In all other respects, the 2 versions were identical, involved the same hypothetical patient, contained a single question, and were randomly assigned. Given the information about symptoms following treatment, participants then decided whether to recommend continuing or discontinuing the treatment. For a few scenarios, we added a third, untested version that gauged participants’ decisions to recommend the treatment before it had been tested.

Several survey features were designed to minimize bias. First, each scenario was conceived on the basis of direct clinical experience to make the case meaningful, original, clear, realistic, and relevant. Second, no identifying demographic information on participants was collected to maintain confidentiality and reduce respondent workload. Third, the scenarios all described a patient who had few comorbidities, thus minimizing complexity and reducing the need for medical expertise. Fourth, each respondent saw only one version of one scenario to reduce boredom or carry-over artifacts. Finally, scenarios were designed to examine widely different common clinical problems, allowing for potential generalizability.

We surveyed different participant groups in North America. The largest group was community participants recruited through the Prolific Survey platform. 12 The second group consisted of active community pharmacists who were contacted in person while in their place of work at off-peak regular business hours. The intent was to elicit judgments from potential patients and from health care professionals who were experienced, informed, engaged, and commonly interacting with patients. Community participants were surveyed in the early winter months of 2023 and 2024, and pharmacists were surveyed in the summer months of 2023.

All participants completed the survey anonymously with a planned time-to-completion of less than 2 minutes. Participants were blinded to the hypothesis, given a single scenario, and unaware of alternative versions. Community participants received the survey online and were compensated at the standard internet platform rate ($15 per hour). Further background details on the community participants appear in the eAppendix in Supplement 1 . The introductory script for the pharmacists was, “I would like to talk with a pharmacist,” with additional contingent replies formulated in advance using methods for secret shopper science. 13 Pharmacists received no incentives (aside from a gracious thank-you).

The study was approved by the Research Ethics Board of Princeton University and the Sunnybrook Research Institute. The approval included a waiver for signed consent, with willingness to participate expressed by continued activity on the platform (and the freedom to discontinue at any point). The pharmacists were contacted in person and later received a debriefing letter that provided an explanation of the research, a statement of what was done, an expression of gratitude, an opportunity for discussion, reassurances of anonymization, contact details for concerns, and the option to withdraw (none subsequently withdrew). This study follows the American Association for Public Opinion Research ( AAPOR ) reporting guideline.

No other groups were involved in the study, and no participants were excluded from analysis. Participants and the public were not involved in the research design or study reporting.

The primary analysis compared responses to the 2 versions of each scenario, expressed as the proportion of respondents who recommended (tend to or definitely) continuing the treatment. Statistical analyses used the χ 2 test and an odds ratio (OR) estimate for a consistent measure of effect size (analyses using the Mann-Whitney test to account for the full nonparametric distribution yielded similar results and appear in the eAppendix in Supplement 1 ). Sample size calculations were designed to provide 80% power (β = 0.20) for detecting an effect size of at least 15% (baseline of 10%, increase to 25%). The analytic plan was specified in advance, each scenario was considered an independent test, all P values were 2-tailed, and OR estimates were accompanied by 95% CIs.

In total, 1497 community members (mean [SD] age, 38.1 [12.5] years; 663 female [55.3%]) were contacted. Additional demographic data for community members are shown in the eAppendix in Supplement 1 . One hundred pharmacists were also contacted.

The first case involved using antibiotic treatment contrary to medical science. The improved and unchanged versions were identical except the bold text (improved version) was replaced by the bold text in brackets (unchanged version): “JL is a 35-year-old schoolteacher. She has a sore throat and starts an antibiotic treatment from an unused prescription offered by a friend. According to medical science, however, misusing antibiotics might eventually cause resistant organisms. The next day JL feels better [unchanged]. Would you recommend she continue or discontinue the antibiotic?”

A total of 300 individuals responded, of whom 150 received the improved version and 150 received the unchanged version. As hypothesized, more recommended continuing the antibiotic after symptoms subjectively improved than when symptoms were unchanged (67 respondents [45%] vs 25 respondents [17%]). This discrepancy equaled a moderate relative increase in continued treatment (OR, 3.98; 95% CI, 2.33-6.78; P  < .001) ( Table ).

This case involved considering an unproven sugar supplement for insomnia. The improved and unchanged versions were identical except the bold text was replaced by the bold text in brackets: “GD is a 45-year-old administrator. She has irregular insomnia and starts a sugar powder supplement in the hopes of getting better sleep. The next week her sleep is better [unchanged]. Would you recommend she continue or discontinue the sugar powder supplement?”

A total of 200 individuals responded, of whom 100 received the improved version and 100 the unchanged version. As hypothesized, more recommended continuing the sugar after symptoms improved than when symptoms were unchanged (83 respondents [83%] vs 17 respondents [17%]). This discrepancy amounted to a large increase in continued treatment (OR, 22.77; 95% CI, 10.99-47.18; P  < .001) ( Table ).

This case involved a family member suggesting an alternative remedy to a young woman with neck pain. The improved and unchanged versions were identical except the bold text was replaced by the bold text in brackets: “KA is a 30-year-old flight attendant. She feels intermittent neck pain and starts acupuncture for relief (suggested by her grandmother). The next week she feels better [unchanged]. Would you recommend she continue or discontinue the acupuncture?”

A total of 198 individuals responded, of whom 100 received the improved version and 98 the unchanged version. As hypothesized, more recommended continuing the acupuncture after symptoms improved than when symptoms were unchanged (87 respondents [87%] vs 29 respondents [28%]). This discrepancy amounted to a large increase in continued treatment (OR, 15.27; 95% CI, 7.46-31.27; P  < .001) ( Table ).

This case involved the role of advertising of commercial products promoted for wrist pain. The improved and unchanged versions were identical except the bold text was replaced by the bold text in brackets: “MB is a 45-year-old accountant. She feels intermittent wrist pain and starts wearing a copper bracelet for pain relief (advertised in a magazine). The next week she feels better [unchanged]. Would you recommend she continue or discontinue the copper bracelet?”

A total of 199 individuals responded, of whom 100 received the improved version and 99 the unchanged version. As hypothesized, more recommended continuing the copper bracelet after symptoms improved than when symptoms were unchanged (78 respondents [78%] vs 25 respondents [25%]). This discrepancy amounted to a large increase (OR, 16.19; 95% CI, 5.32-19.52; P  < .001) ( Table ).

This case involved an outlandish treatment directly contrary to research evidence. The improved and unchanged versions were identical except the bold text was replaced by the bold text in brackets: “DK is a 75-year-old retired professor. He is going bald and starts applying horse shampoo once per day. However, researchers typically agree horse shampoo does not promote hair growth. A couple of weeks later his hair seems better [unchanged]. Suppose he is undecided and asks you, would you recommend DK continue or discontinue the horse shampoo?”

A total of 400 individuals responded, of whom 200 received the improved version and 200 the unchanged version. As hypothesized, more recommended continuing the horse shampoo after symptoms improved than when symptoms were unchanged (129 respondents [65%] vs 14 respondents [7%]). This discrepancy amounted to a very large increase in continued treatment (OR, 23.30; 95% CI, 12.69-42.16; P  < .001) ( Table ).

This case involved considering a vitamin supplement for fatigue. The improved and unchanged versions were identical except the bold text was replaced by the bold text in brackets: “WS is a 30-year-old hospital nurse. He feels tired and starts a Vitamin B12 supplement for energy (despite normal B12 levels). The next week he feels better [unchanged] . Would you recommend he continue or discontinue the Vitamin B12 supplement?”

A total of 200 individuals responded, of whom 100 received the improved version and 100 the unchanged version. Despite the normal baseline B 12 level, many respondents recommended continuing the vitamin B 12 supplement. As hypothesized, more recommended continuing after symptoms improved than when symptoms were unchanged (80 respondents [80%] vs 33 respondents [33%]). This discrepancy equaled a substantial relative increase in treatment (OR, 7.91; 95% CI, 4.18-14.97; P  < .001) ( Table ).

The replication study involved a situation similar to the preceding fatigue scenario, this time enacted by a secret shopper visiting different pharmacies and asking the pharmacists for advice. The improved and unchanged versions were identical except the bold text was replaced by the bold text in brackets. The wording was, “Hi, I am wondering if I can talk with a pharmacist? [Wait for pharmacist.] Last week I was feeling tired and started taking a Vitamin B12 supplement for energy. Now this week I feel better [unchanged] . Do you think I should continue or discontinue it?”

A total of 100 pharmacists were contacted, of whom 44 and 56 received the improved and unchanged versions, respectively. More pharmacists recommended continuing the treatment after symptoms improved than when the symptoms were unchanged (36 pharmacists [82%] vs 35 pharmacists [63%]). This discrepancy equaled a modest significant relative increase in recommended treatment (OR, 2.60; 95% CI, 1.04-6.52; P  = .03) ( Table ).

Through scenarios involving diverse conditions, this survey study identified a persistent tendency toward continuing a treatment when initial care was followed by some subjective improvement. Although a modicum of enthusiasm might be expected, the substantially increased preferences for continuation are unwarranted because the improvement is not necessarily caused by the treatment. The post hoc bias might both increase a patient’s willingness to continue and also to recommend the treatment to others. The same error might also lead to undertreatment if a random adverse event leads to a worsening of symptoms. 14 These strong patient intuitions are misplaced because the situation is not a randomized trial sufficient to prove effectiveness. 15

A reliance on the post hoc bias might be an expedient compromise in some cases. Sometimes the mistake may be harmless when undue treatment is relatively safe and entirely affordable. It can also be innocuous for patients with self-limited diseases when no other critical diagnoses or interventions are missed. In addition, a post hoc bias might solidify a doctor-patient relationship by exemplifying attention, compassion, and continuity of care. Continuing treatment after an improvement also aligns with the traditional aphorism, “don’t mess with success.” Abiding by a modicum of post hoc bias is sometimes the most straightforward position, whereas the effort to resolve the misunderstanding is not always worth the trouble.

Continuing a treatment is, of course, sometimes also warranted. Some treatments are definitive, such as bullet extraction for saving a patient from a gunshot wound. In other cases, the reasoning is correct owing to enormous effectiveness, such as antiviral drugs for HIV infection. Some treatments are convincing with long-term follow-up (eg, hip replacement for advanced osteoarthritis), and other treatments are confined to extreme cases (eg, cardiac defibrillation for a cardiac arrest). Many metabolic disorders can be cured, such as thyroid replacement for hypothyroidism. Intuitive judgments about effectiveness, however, can be mistaken when they overlook the contribution of rest, attention, time, or other factors irrelevant to the treatment.

Our study highlights a core weakness around intuitions in health care. The simple logic of cause-and-effect reasoning implies that action leads to outcome; however, human biology is complex, with many active factors influencing a single patient. These factors include surrounding supportive care (medications, fluids, diet changes, and physiotherapy), as well as unobserved cointerventions (cessation of adverse behaviors, separation from harmful environments, physiologic homeostasis, and placebo effects). Furthermore, simple regression to the mean can lead to improvement, since people often resort to dubious treatments when things get worse. 16 Simplifying patient outcomes to a single medical intervention is arguably the fundamental fallacy emblematic of reductionist perspectives. 17

Clinicians often acknowledge complexity in the aftermath of failure—namely, patient outcomes are uncertain. That same humility can feel less likely in the aftermath of a success where adherence to scientific discipline can be difficult following a treatment initiated by the responsible clinician. 18 Personal motivations might further reinforce undue confidence after an improvement since clinicians want to believe their work makes a positive difference. 19 Faulty attribution may be further exacerbated when the clinician interacts with a grateful patient, appreciative family, and impressed colleagues. In short, the system surrounding care helps perpetuate the post hoc bias.

Our study focused on the tendency to continue a dubious treatment, whereas post hoc bias might also lead to prematurely stopping an appropriate treatment. Specifically, we examined an additional, untested version of some scenarios where a treatment was under consideration but not yet tried. In line with post hoc bias, we found reduced recommendations for treatment after no improvement occurred compared with before the treatment was initiated. In the acupuncture scenario, for example, few recommended continued treatment in the unchanged version, despite most recommending treatment in the untested version (eAppendix in Supplement 1 ). Similarly, in the vitamin supplement scenario, fewer participants recommended treatment when no immediate change occurred compared with the untested version. Apparently, enthusiasm can quickly diminish after no signs of improvement.

A tendency to prematurely stop a treatment is another facet of post hoc bias that serves to illuminate an important point. In some cases, a placebo effect might be sufficient justification to continue a harmless treatment, and a nocebo effect might justify discontinuation after an adverse event. 20 It is noteworthy, however, that situations such as the antibiotic case and the sugar supplement case raise adverse consequences beyond the putative benefits of a placebo effect. Furthermore, neither a presumed placebo effect nor a nocebo effect can explain the post hoc tendency to discontinue treatment after no change. The post hoc tendencies we observe following no change in symptoms serve to illustrate that placebo considerations cannot explain post hoc bias more generally.

Our study has several limitations that merit mention. We examined simple decisions, whereas medicine is often more complicated. The study involved brief scenarios with time for thought, whereas clinical settings often involve distractions, uncertainties, and conflicting priorities. We did not collect baseline characteristics on participants or conduct interviews inquiring about their reasoning. Future research could also test whether post hoc bias can be mitigated by patient education or whether it extends more broadly (eg, when symptoms worsen or treatment effects are delayed). To be sure, our randomized design renders the results difficult to attribute to atypical respondents, sloppy responses, strategic ploys, fallible information, or random chance.

We tested a small number of scenarios, whereas post hoc bias might extend to a variety of faulty beliefs that worsen with experience. Similar to other pitfalls in reasoning, post hoc bias may be more easily detected in others than in oneself and might otherwise persist despite personal experience. 21 Years of practicing bloodletting in the history of medicine, for example, led to some patients improving by chance and to reinforcing anecdotes of successful treatment. 22 Similarly, modern care abounds with patients who blame vaccines for subsequent unrelated illnesses. 23 , 24 Interestingly, the opposite of post hoc bias might occur in preventive medicine, where the absence of an event is often unrecognized and rarely attributed to an earlier intervention.

We found that a marginal improvement in symptoms tends to encourage patients to continue a treatment even when the change might be coincidental. Similarly, a lack of improvement can lead patients to discontinue treatment too soon. Akin to other heuristics, this intuition is sometimes appropriate and not always mistaken. Reliance on post hoc reasoning is logical when a diagnosis is certain and treatment infallible; otherwise, post hoc bias can contribute to complacency, discourage the search for alternatives, and lead to shortfalls in care. An awareness of post hoc bias will not make it disappear; however, it might help if clinicians reminded patients that symptomatic improvements still merit considering alternative explanations.

Accepted for Publication: July 3, 2024.

Published: September 4, 2024. doi:10.1001/jamanetworkopen.2024.31123

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Redelmeier DA et al. JAMA Network Open .

Corresponding Author: Donald A. Redelmeier, MD, MSHSR, Evaluative Clinical Sciences Program, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, G-151, 2075 Bayview Ave, Toronto, ON M4N 3M5, Canada ( [email protected] ).

Author Contributions: Dr Redelmeier had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Both authors.

Acquisition, analysis, or interpretation of data: Both authors.

Drafting of the manuscript: Both authors.

Critical review of the manuscript for important intellectual content: Both authors.

Statistical analysis: Both authors.

Obtained funding: Both authors.

Administrative, technical, or material support: Both authors.

Supervision: Both authors.

Conflict of Interest Disclosures: None reported.

Funding/Support: This project was supported by the Alfred P. Sloan Foundation (grant 2014-6-16), a Canada Research Chair in Medical Decision Sciences (grant 950-231316), the Canadian Institutes of Health Research (grant 436011), the PSI Foundation of Ontario (grant 2214), and the National Science Foundation (grant SES-1426642).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The views expressed are those of the authors and do not necessarily reflect the Ontario Ministry of Health.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: Jonathan Wang, MMASc (University of Toronto), performed data collection based on community pharmacies and provided helpful suggestions on manuscript revisions. Bill Bartle, PharmD (University of Toronto), Vidhi Bhatt, BSc (University of Toronto), Daniel Kahneman, PhD (Princeton University), Fizza Manzzor, MD (Harvard University), Shina Namakian, BSc (McMaster University), Sheharyar Raza, MD (University of Toronto), Raffi Rush, MD (University of Toronto), Steven Shumak, MD (University of Toronto), and Barbara Sibbald, BA (Canadian Medical Association), provided helpful suggestions on manuscript revisions. They were not compensated for this work.

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Conceptualizing and Advancing Research Networking Systems

Science in general, and biomedical research in particular, is becoming more collaborative. As a result, collaboration with the right individuals, teams, and institutions is increasingly crucial for scientific progress. We propose Research Networking Systems (RNS) as a new type of system designed to help scientists identify and choose collaborators, and suggest a corresponding research agenda. The research agenda covers four areas: foundations, presentation, architecture , and evaluation . Foundations includes project-, institution- and discipline-specific motivational factors; the role of social networks; and impression formation based on information beyond expertise and interests. Presentation addresses representing expertise in a comprehensive and up-to-date manner; the role of controlled vocabularies and folksonomies; the tension between seekers’ need for comprehensive information and potential collaborators’ desire to control how they are seen by others; and the need to support serendipitous discovery of collaborative opportunities. Architecture considers aggregation and synthesis of information from multiple sources, social system interoperability, and integration with the user’s primary work context. Lastly, evaluation focuses on assessment of collaboration decisions, measurement of user-specific costs and benefits, and how the large-scale impact of RNS could be evaluated with longitudinal and naturalistic methods. We hope that this article stimulates the human-computer interaction, computer-supported cooperative work, and related communities to pursue a broad and comprehensive agenda for developing research networking systems.

1. INTRODUCTION

Over the past several decades, science has become significantly more collaborative [ Adams et al. 2002 ; Arzberger and Finholt 2002 ; Katz and Martin 1997 ; Rhoten 2007 ; Zerhouni 2003 ]. Increases in the number of international collaborations, coauthored papers, and multi-investigator grant proposals are evidence for this trend [ Olson et al. 2008a ], as is the rising frequency of terms such as “interdisciplinarity” and “multi-disciplinarity” in the literature [ Braun and Schubert 2003 ]. Olson et al. cite multiple reasons for this development: “the urgency, complexity and scope of unsolved scientific problems; the need for access to new, and often expensive, research instruments and technologies; pressure from funding agencies; and information and communication technologies that facilitate interaction and sharing” [ Olson et al. 2008a ]. Therefore, collaboration among the right individuals, teams, and institutions is becoming ever more crucial for progress in science.

Finding “optimal” (regardless of how one defines the term) collaborators, however, is difficult, and becoming more so [ Schleyer et al. 2008a ; 2008b ; Spallek et al. 2008 ]. Establishing collaborations is a labor-intensive and risky process, especially when multiple disciplines are involved. Collaboration seekers often struggle with the target disciplines’ terminology, have difficulty identifying true experts, and lack relevant social contacts. In addition, they must assess potential collaborators in light of many criteria [ Schleyer et al. 2008b ], a process impeded by incomplete, fragmented information. Finally, reviewing potential collaborators does not scale well. Assessing the n th candidate takes as much work as assessing the first. At the same time, the universe of collaborative opportunities continues to expand as information about researchers becomes more accessible and remote collaborations become more feasible [ Katz and Martin 1997 ].

Recently, the term “Research Networking Systems” (RNS) became popular to describe electronic systems designed to help researchers find collaborators. “Research networking system” emerged as an alternative to “research collaborator discovery system,” “expertise location system,” and other terms after the National Center for Research Resources awarded a $12m grant to the University of Florida to develop a national prototype system 1 . The request for applications solicited proposals to develop “infrastructure for connecting people and resources to facilitate national discovery of individuals and of scientific resources by scientists and students to encourage interdisciplinary collaboration and scientific exchange” [ National Center for Research Resources 2009 ].

In light of this goal, we propose the following definition for RNSs.

Research Networking Systems (RNS) are systems which support individual researchers’ efforts to form and maintain optimal collaborative relationships for conducting productive research within a specific context.

Several aspects of the definition are noteworthy. While RNSs can serve other purposes, such as managing a university’s research portfolio, the primary users whose needs must be met are “individual researchers.” RNSs are intended to help “form and maintain” relationships, not complete collaborative tasks. “Collaborative relationships” refer to the interpersonal ties that support successful research collaborations. While the nature of these relationships is subject to ongoing debate, our definition assumes that they involve shared, two-way interests; ongoing, often sporadic, interaction; and the creation of joint work products. “Optimal” is a subjective and situational measure, yet searching for the best possible opportunities is central to RNSs. The aspect of “productive” research speaks to the collaboration outcomes. While papers, presentations, and other scientific artifacts are generally accepted metrics of research productivity, they are arguably imperfect. Lastly, “context” is included in the definition of RNSs because of its importance in shaping research collaborations. Context includes factors such as researchers’ needs and goals, project characteristics, organizational policies, disciplinary norms, and institutional constraints. Successful RNSs provide the information individual researchers need to develop and maintain contextually embedded collaborative relationships.

The goal of this article is to stimulate foundational research on research networking systems that takes into account what is known about collaboration, expertise location, and social networking. We hope to challenge researchers in multiple fields by proposing claims and corresponding research questions that can be tested and/or investigated.

Two considerations must be mentioned to put the proposed research agenda in context. First, in reviewing the literature, we draw from studies of many scientific disciplines, including computer science and biomedicine. Disciplinary culture, values, and norms have a significant impact on collaborative relationships. In order to frame the discussion, this article uses examples from biomedical research. In consequence, the relative importance of the issues we identify may vary in other disciplines. Second, we discuss RNSs primarily in the context of academic research. While the proposed research agenda may be applied to corporate research and development environments, academic research domains are complex and distinct enough to merit separate consideration.

2. RESEARCH NETWORKING AND COLLABORATOR DISCOVERY

Literature relevant to RNSs includes topics such as expertise location systems, formation of scientific collaborations, and the use of technology in research collaborations and social networking. In particular, research on expertise location and sharing [ Ackerman et al. 2003 ; McDonald and Ackerman 1998 ] informs the discussion of RNSs because finding collaborators involves searching for individuals with specific expertise. We therefore review prior work on expertise location systems before discussing existing RNSs.

2.1. Expertise Location vs. Research Networking

Expertise location is a concern in several contexts, including “expertise locating systems” [ McDonald and Ackerman 2000 ], “knowledge communities” [ de Vries and Kommers 2004 ; Erickson and Kellogg 2003 ], and “communities of practice” [ Johnson 2001 ; Millen et al. 2002 ]. Zhang et al [2007] defined Expertise Locator Systems (ELS) as “CSCW systems that help find others with the appropriate expertise to answer a question.” In a review of contemporary ELSs, Becerra-Fernandez [2006] described them as knowledge sharing systems that “point to experts, those that have the knowledge.” Others have defined ELSs in terms of the functions they perform. For example, ELSs can “connect people to people; link people to information about people; identify people with expertise and link them to those with questions or problems; identify potential staff for projects requiring specific expertise; assist in career development; and provide support for teams and communities of practice” 2 .

The CSCW literature contains numerous references to expertise location and the design of expertise location systems [ Ackerman and Palen 1996 ; Ehrlich et al. 2007 ; Friedman et al. 2000 ; Jacovi et al. 2003 ; Mattox et al. 1999 ; McDonald and Ackerman 2000 ; Mockus and Herbsleb 2002 ; Streeter and Lochbaum 1988 ]. This body of work can help us compare and contrast ELSs and RNSs.

First, locating an expert and establishing a research collaboration both involve looking for and discovering expertise. The focus of expertise location is finding an answer, a solution, or a person with whom details of a problem can be discussed [ Ehrlich et al. 2007 ]. The need is largely determined by the task at hand. This emphasis is reversed when forming research collaborations. Researchers looking for collaborators primarily seek a person to establish a relationship with. The specific task or problem is secondary to forming and maintaining this relationship.

Second, the comparatively shorter time horizon of interaction in expertise location allows for benefits which are more asymmetrically distributed. Individuals looking for an answer often stand to gain more than the experts providing it [ Lakhani and von Hippel 2003 ]. In research collaborations, on the other hand, benefits must be more evenly distributed because they often span multiple collaborative tasks and projects, and extended time frames.

Third, ELSs are designed for situations where the goal is defined but needed knowledge is “hidden.” To succeed, individuals must extract answers from the set of available experts. In contrast, scientific researchers often work with ill-defined questions and objectives that shift over time. These collaborative relationships reflect the nature of scientific inquiry in which large problems are pursued incrementally in a meandering, exploratory fashion. The query-driven approach is complemented by an opportunity-driven one, with new directions emerging serendipitously as methods and concepts developed in one area find novel uses in another.

Last, in industry, where most ELSs are deployed, individuals typically work within a single organization. Project assignments, team memberships, and immediate colleagues are determined by management. In academia, scientists often work across institutional boundaries [ Cummings and Kiesler 2005 ; Olson et al. 2008b ] and have significant autonomy when selecting their projects, affiliations, and collaborators.

In summary, while ELSs and RNSs have common functions, they also differ significantly with respect to user characteristics, organizational context, and the goals they serve. Thus, while prior work on ELSs provides a useful starting point for discussions of RNSs, we must also consider systems specifically designed for supporting research networking.

2.2. Current Research Networking Systems

While there is a relatively large body of literature on expertise location systems [ Ackerman et al. 2003 ; Becerra-Fernandez 2006 ], studies of research networking systems are rare. A focused literature search identified descriptions of only five systems which have been tested and/or implemented. Several other recently developed systems have not been described in the literature.

At the University of Pittsburgh, an application called Faculty Research Interests Project (FRIP) helps faculty establish collaborations [ Friedman et al. 2000 ]. FRIP indexes faculty research interests using Medical Subject Headings (MeSH) [ Coletti and Bleich 2001 ] and draws on MEDLINE-indexed publications to populate its database. In 2000, FRIP indexed 1,925 research faculty at the six schools of the University of Pittsburgh’s Health Sciences Center. FRIP’s functionality is currently being replaced by Pitt’s Digital Vita (see the following) system.

A second recently developed tool for helping connect researchers with shared interests is a Facebook application called MEDLINE Publications (MP) [ Bedrick and Sittig 2008 ]. The system uses the PubMed database to automatically create user-customizable lists of publications. The system includes a rudimentary recommendation algorithm to identify other users with similar publication profiles. Like FRIP, MP uses MeSH as the controlled vocabulary for specifying research interests. MP has attracted a reasonable user base, and anecdotal evidence suggests that it has been useful to some.

A third research networking system is Searchable Answer Generating Environment (SAGE), a searchable repository of funded research information for all universities in Florida [ Becerra-Fernandez 2006 ]. This system implements a distributed database schema that can be searched by criteria such as research topic, investigator name, funding agency, and university. To keep the data repository current, participating institutions must provide funding data on an ongoing basis. Researchers across Florida benefit from SAGE increasing their visibility and facilitating efforts to locate potential collaborators at other universities, in industry, and in federal agencies. SAGE has also been used by NASA and small businesses to identify university researchers for collaboration. As of 2006, the SAGE database included about 7,817 researchers and 53,124 projects from fourteen institutions throughout Florida.

Liu et al. [2005] described a system that uses RDF (Resource Description Framework) for expertise matching by integrating data from multiple, heterogeneous sources and making them available through concept-based searches. An initial prototype system was evaluated in the School of Computing at the University of Leeds. Results indicate that the RDF-based expertise matching system outperforms traditional DBMS techniques because it improves match accuracy and facilitates expertise selection.

Last, Schleyer and colleagues [2008a] proposed the Digital Vita system as a prototypical design and architecture responsive to initial requirements for research networking [ Schleyer et al. 2008b ]. Digital Vita includes four main functions: maintaining, formatting, and semiautomated updating of biographical information; searching for researchers; building and maintaining social networks; and managing document flow. The system departs from other approaches for representing researchers in that it is built around a researcher’s academic Curriculum Vitae (CV). While not perfect, the CV is often the most up-to-date and comprehensive document describing a scientist’s accomplishments and activities. With its focus on CV maintenance, integration with the local context, and provision of benefits for individual researchers, Digital Vita has the potential to reduce adoption barriers, represent researchers more comprehensively than keyword-based profiles, and achieve ongoing system utilization.

In addition to the five systems described in the literature, several other research networking systems exist in academia and industry. Academic systems include the University of Florida’s VIVO 3 project [ Gewin 2010 ], Harvard’s Catalyst Profiles 4 , and the University of Iowa’s Loki 5 . The Distributed Interoperable Research Experts Collaboration Tool (DIRECT) 6 is a recent initiative to allow users to search for experts across these systems. Commercial systems include the Community of Science ( http://www.cos/com ), Index Copernicus Scientists ( http://scientists.indexcopernicus.com/ ), Research Crossroads ( http://www.researchcrossroads.com/ ), BiomedExperts ( http://www.biomedexperts.com/ ), and Epernicus ( http://www.epernicus.com ).

Each of these systems has a different approach for creating searchable directories of researchers. As a result, they provide useful insights into the architectural and data management problems associated with gathering and storing researcher profiles. However, as with expertise location systems, the research networking systems described in the literature only partially address the requirements of research networking.

2.3. Research Networking Challenges in Biomedical Sciences

While the marketplace and academic institutions have begun implementing expertise-focused research networking systems, there is a need for theories and models to inform RNS design, implementation, and evaluation. No extant studies directly consider RNSs. Nonetheless, the literature on scientific collaboration and collaboration formation provides some insight into the problems that RNSs are intended to address.

A recent study by Weng et al. [2008] showed that collaboration on cross-cutting research topics such as obesity is not well served by the traditional organization of biomedical research institutions. The authors identified obesity researchers using several search strategies (Google, PubMed, and snowball sampling) and surveyed them to determine departmental/center affiliation, collaborators, and research interests. Participants were distributed over multiple departments and often affiliated with more than one research center. Respondents who collaborated with others had 8.8 collaborators on average, indicating a relatively active community. Some research groups, however, were only connected by a single pair of individuals. Institution-level success factors for interdisciplinary collaboration suggested by the study included “(1) establishment of interdisciplinary research centers; (2) identification of boundary spanners who link dispersed research communities; and (3) creation of scientific journals that publish transdisciplinary research results.” The findings of this study suggest that interdisciplinary collaborations could be organized as “virtual teams” [Hinds et al. 2002].

In a more general attempt to understand how research collaborations are formed in the health sciences, Spallek and colleagues [ Schleyer et al. 2008a ; 2008b ; Spallek et al. 2008 ] conducted semistructured interviews with 27 biomedical scientists at the University of Pittsburgh. The study focused on general aspects of subjects’ collaboration activity, such as who they were currently collaborating with, what motivated them to seek collaborators, and how they searched for them. Four main groups of factors were found to affect collaboration-seeking: motivation, evaluation, search and selection, and barriers. Participants who reported using directories such as FRIP or Community of Science noted that they were useful for people new to an institution and for finding individuals outside the immediate work context. However, researcher directories were seen as limited because of incomplete coverage of research domains; sparse, outdated researcher profiles; and lack of support for leveraging social networks. Although this study did not focus on research networking systems, its results suggest that developing and refining such systems would have significant practical utility.

In parallel, our research group also formulated an initial set of requirements for collaborator discovery systems in biomedical science [ Schleyer et al. 2008b ]. The study used affinity diagramming, literature reviews, contextual inquiries, and semistructured interviews to develop a list of requirements for systems for finding collaborators. The requirements include: a good cost/benefit ratio for the user when creating and updating online profiles; representation of researchers through rich, comprehensive, and up-to-date information; exploitation of social networks; assessment of potential collaborators’ “soft” traits, such as personality and work styles; use of multiple indicators of past collaboration activity; user-modifiable preferences regarding privacy and public availability of profile information; effective cross-disciplinary search; and active highlighting of “nonintuitive” connections between researchers.

Existing studies show that RNSs must function in a complex socio-technical context. They are subject to multiple, sometimes conflicting, requirements that must be balanced carefully in order to maximize system utility for all user populations. While there is a growing body of work which examines the factors underlying effective research collaborations, many unanswered questions remain about how to best use information technology to facilitate research networking.

3. RESEARCH AGENDA FOR RESEARCH NETWORKING SYSTEMS

The following research agenda is organized around four areas that contribute to RNS success: foundations, presentation, architecture , and evaluation . Foundations addresses theoretical models, core principles and general factors that underlie the design of effective RNSs. Presentation examines issues concerning user interfaces, interaction design, and system functionality. Architecture discusses the internal design of RNSs, how they interact with external information sources, and interoperability. Finally, evaluation is concerned with how RNS outcomes can be framed and measured.

While the proposed categorization of the particular claims and research questions may be debated, the four areas are critical aspects of RNS design and implementation. They support both targeted investigation of issues and identification of useful links to the diverse body of existing research. In each area, we posit claims regarding the nature of collaborative relationships and RNSs. Each claim is followed by a brief review of the relevant literature and a list of open questions which, if addressed, would significantly improve our ability to design, implement, and evaluate research networking systems. The goal of this research agenda is to advance the study and development of RNSs, and to make them a useful part of the scientific enterprise. Hence, the open questions were selected to focus attention on issues particular to RNSs as opposed to related systems, such as virtual communities, expertise location, and cooperative work.

3.1. Foundations

While it may be convenient from a systems design perspective to conceptualize research networking as a search or information display problem, RNSs must support a more complex set of social behaviors. In this section we describe three foundational perspectives on collaborative relationships, and examine their implications for the design and evaluation of RNSs.

To form collaborative relationships, individuals must balance the different motivations of potential collaborators in the context of projects, institutions, and disciplines.

Many researchers have proposed models for describing effective collaborations [ Suchman and Trigg 1986 ]. Existing frameworks focus on various aspects of collaboration, including key concepts/variables at work in research collaborations [ Katz and Martin 1997 ; Larson 2003 ; Melin 2000 ; Suchman and Trigg 1986 ], participants in a collaboration and the division of labor [ Jenerette et al. 2008 ; Kouzes et al. 1996 ], and the process of collaboration and activities involved at each stage [ Gitlin et al. 1994 ; Kraut et al. 1987 ]. In part, this body of work has also explored the motivations and mechanisms underlying collaboration formation.

At societal level, researchers have examined the transformation of modern science and the social, cultural, and technological factors that drive collaboration [ Börner et al. 2010 ]. These factors include use of expensive, sophisticated instrumentation [ Olson et al. 2008a ]; more emphasis on application; greater specialization and concentration of resources [ Ziman 1994 ]; changing patterns and levels of funding; and the growing professionalism of science [ Katz and Martin 1997 ]. However, the move towards a greater degree of collaboration in science is not without problems [ Cummings and Kiesler 2007 ]. Multi-university collaborations face significant coordination challenges which, if not addressed, can lead to suboptimal project outcomes [ Finholt and Olson 1997 ].

At project level, factors that affect collaboration include problem complexity and scale, division of labor, and degree of specialization [ Laudel 2002 ; Rhoten 2007 ]. Institutional factors that influence collaboration activity include role specialization [ Madanmohan and Navelkar 2004 ], the nature of the work [ Birnholtz 2007 ], the radicalness of the research [ Belkhodja and Landry 2007 ], access to particular resources [ Mattessich and Monsey 1992 ], structural characteristics of organizations [ Walsh and Maloney 2007 ], organizational processes [ LeGris et al. 2000 ], organizational management and support [ Millen et al. 2002 ], and funding contingencies [ Bos et al. 2007 ]. Many things which motivate individual scientists to collaborate, such as the need for knowledge, expertise, and skills [ Beaver 2001 ]; access to special equipment and funding [ Melin 2000 ]; the desire for social relationships [ Fox and Faver 1984 ; Terveen and McDonald 2005 ]; and the need to educate and mentor students [ Katz and Martin 1997 ; Melin 2000 ], are directly linked with these project and institutional factors.

As with any long-term relationship, collaborations can only be maintained if the work and incentive structures are aligned so that all of the involved individuals benefit from participation [ Numprasertchai and Igel 2005 ]. Successful RNSs must support individuals’ efforts to identify potential collaborators whose needs and incentives complement their own. Through rich user models and appropriately designed profiles, RNSs can leverage information about institutional, project, and individual factors to help collaboration seekers’ detect when and where collaboration is useful and feasible. This suggests that the following questions are central to the design and creation of effective RNSs.

• —How should RNSs model user characteristics known (or hypothesized) to affect willingness of individuals to engage in collaborations? While a variable such as “age” is easy to model, others like “seniority” or “technical competence” are more difficult to represent.
• —How should RNSs incorporate project-related, institutional, social, structural, and cultural characteristics which affect individuals’ motivation to participate in collaborative relationships?
• —How should RNSs model the conditions under which researchers start looking for a collaborator? Is a single model sufficient? How should the model evolve over time as careers, accomplishments, and interests change?

Exploiting social networks is essential for efficient and effective research networking.

People work within social networks. Although these networks may cross organizational boundaries and span geographic distance, individuals are still constrained by who they know and what they know about them. As a result, many expertise location systems developed in recent years have integrated social network information to help evaluate potential experts and facilitate communication with them [ Kautz et al. 1997a ; Ogata et al. 2001 ]. McDonald [2003] compared two different social networks as alternative bases for recommending experts within a medical software company. The first network, based on shared work contexts, captured network ties arising from work arrangements. The second, the socializing network, linked individuals who interacted socially. The results illuminated a number of critical issues to consider in development of RNSs. Using network information forced a trade-off between finding the most knowledgeable person and finding the person with whom the searcher could most easily interact. Also, users still sometimes desired broader recommendations even if the system’s recommendations were appropriate. Lastly, users often preferred their own egocentric social network over the one generated and recommended by the system.

In another system, Yang and Chen [2008] developed a mathematical model of a three-layer social network to support interactive collaboration, taking into account the knowledge relationship and social relationship ties of potential collaborators. In this system, a peer-to-peer knowledge net is overlaid with the peer-to-peer social net. An Instant Messaging (IM) system helps individuals communicate with peers identified through the social network. Preliminary evaluation of this system with student users showed that most were willing to use this system to find others open to sharing their knowledge.

Many methods for gathering social network information have been suggested. Social networks have been constructed based on email exchanges among individuals [ Ogata et al. 2001 ], Web pages related to a person, the Database systems and Logic Programming (DBLP) bibliographic information service for computer science, and the publication ranking list from Citeseer [ Li et al. 2007 ]. Pavlov and Ichise [2007] built link predictors which identify potential collaboration opportunities using the structural information in coauthorship networks. However, social networks derived from coauthorship are likely to be imperfect representations of a researcher’s collaborative relationships [ Katz and Martin 1997 ]. To overcome this problem, McDonald and Ackerman [2000] used participant observation, formal and informal interviews, and pile sorts. Yang and Chen [2008] had users fill out forms and answer questions about peers’ knowledge and social ties. The Digital Vita system blends the two strategies, allowing researchers to specify collaborative relationships explicitly through “colleague requests” (equivalent to “friend requests” in Facebook) [ Schleyer et al. 2008a ] while also deriving implicit ties such as coauthorship and shared department membership from CVs.

In traditional social networks, individuals rely on their contacts to provide access to a wide range of information and opportunities [ Adler and Kwon 2002 ]. Supporting searches within a network is an important part of facilitating collaboration formation. Previous research on search strategies in social networks has identified two main approaches. The first is automation of the small-world approach, where the target is known by name or a unique identifier [ Adamic and Adar 2005 ; Yang and Garcia-Molina 2002 ]. Adamic and Adar [2005] simulated small-world experiments on an email network in an organization and a student social networking system Web site. They found that small-world search strategies using a contact’s position in physical space or an organizational hierarchy could effectively locate the most appropriate individuals. However, in a social network where hierarchical structures were not well defined, local search strategies were less effective.

A second approach for searching within a social network focuses on locating a person with specific expertise or knowledge. Zhang and Ackerman [2005] evaluated three families of strategies for searching using social network information. These strategies were based on computation, network structure, or individual similarity. The computational approach, for instance, used breadth-first Search to broadcast a query to a person’s neighbors. Information scent search, on the other hand, selected the person with the highest match score between the query and his profile [ Yu and Singh 2003 ]. In a simulation on an organization’s email dataset, the different strategies affected the search process in important ways. For example, weak ties [ Granovetter 1973 ] appeared more effective for seeking new information, but the relative rank of different algorithms changed little when examining social costs.

The importance of existing network structures in formation of collaborations suggests that the following questions are critical for design of effective RNSs.

• —How can information about researchers’ social and collaborative networks be gathered and maintained efficiently? How can implicit relationships, such as coauthorship, be refined and/or augmented to serve as a basis for constructing social networks?
• —How can explicit relationship identification be applied in RNSs? Should network size be limited to avoid “colleague inflation”?
• —How should social network data be used to support collaboration seeking? Should users be encouraged to focus on relatively small social distances [ Schleyer et al. 2008a ] or explore lengthy referral chains [ Kautz et al. 1997b ]?
• —How should existing and potential collaborative relationships be represented? Should weak ties be distinguished from (and perhaps given priority over) strong ties? Should potential collaborators be ranked based on the number of current collaborators (i.e., network degree)?
• —How can boundary-spanning individuals be identified and leveraged in order to generate collaboration opportunities?

Establishing collaborations requires individuals to form impressions of and evaluate potential collaborators based on information beyond expertise and interests.

In the research collaboration literature, few studies have focused on the initiation of collaborations. Kraut et al. [1987] suggest that collaboration formation is more a process than an event. The initiation stage involves both relationship- and task-related activities. For the relationship, the essential activity is determining whether potential collaborators are acceptable partners. At task level, participants must identify collective research objectives and formulate specific work plans. If a collaboration is to succeed, researchers must develop from mere acquaintances to committed partners. Kraut identified two paths for this process. For some researchers, the initial contact evolves into joint commitment the way a bilateral friendship develops. For others one partner proposes collaboration just like during an asymmetric courtship ritual. Whichever way collaborations develop, serendipitous and informal conversations are an important early step.

Prior studies have examined a variety of factors that affect how prospective collaborators are evaluated, including competence, complementarity, work and collaboration styles [ Axelrod 1984 ], personality, and physical proximity [ Kraut et al. 1987 ]. Schleyer et al. [2008b] identified support for compatibility assessment as a key element of RNS requirements. For instance, the system should enable users to find collaborators compatible in personality, work style, and other factors. An individual’s likely availability, accessibility, and willingness to engage in collaboration also might impact her selection as a potential collaborator. Several studies found that researchers trust personal recommendations when assessing compatibility with potential collaborators [ Beaver 2001 ; Flynn 2005 ]. Interaction with potential collaborators is another way researchers gather information about their compatibility. Face-to-face interaction seems to produce the highest trust among unfamiliar collaborators [ Moore et al. 1999 ]. In the absence of face-to-face opportunities, strategies such as chat sessions and exchange of personal information can also help to overcome limited availability of information [ Zheng et al. 2002 ]. While there are a range of evaluation criteria, their relative importance appears to be context- and situation-dependent. For instance, work style compatibility may only be a minor constraint for a collaboration based on sharing equipment or other scarce physical resources [ Spallek et al. 2008 ].

While much research in the expertise location system literature has focused on expertise representation, there is evidence from studies of work relationship formation that expertise may sometimes be a secondary concern when selecting collaborators [ Casciaro and Lobo 2005 ; 2008 ]. Studies of social matching, such as the work of Terveen and McDonald [2005] , show that personal characteristics must be taken into account during the matching process. This suggests that providing information, either directly or indirectly, about traits such as personality, friendliness, character, trustworthiness, sense of humor, and work style may be relevant in the design of RNSs. The apparent difficulty of obtaining information about these traits is one reason why social connections are so important in collaborator discovery: they can be a source of information about personal traits. The importance of this information in collaborative relationship formation suggests that the following research questions are central to the study of RNSs.

• —What collaborator traits, other than expertise and interests, are useful in making collaboration decisions?
• —How can traits such as productivity, work style, adherence to deadlines, organization, communication style, conflict resolution skills, and personality be assessed, modeled, and presented? Which traits should be highlighted in interfaces designed to support evaluation of potential collaborators?
• —What features and technologies are best suited for supporting joint exploration of relationship and task issues during the initial stages of collaboration formation?

While they are not the only possible characterizations of collaborative relationships, the three theoretical perspectives presented here (collaborative relationships as balanced incentive structures; embedded social ties; and the result of impression formation) provide a foundation for defining requirements for RNSs.

3.2. Presentation

At their core, RNSs are systems that capture, store, and present data about people and relationships. The interface used interact with these data significantly affects how an RNS influences users’ efforts to form and maintain collaborations. In this section, we consider aspects of presentation and representation that theory and prior work suggest will be critical for the creation of successful RNSs.

RNS must describe potential collaborators’ expertise and interests in a comprehensive and up-to-date manner.

Early attempts at compiling representations of expertise relied on data provided by the user, typically in the form of profiles. HelpNet, for instance, asked users to fill in and maintain profiles [ Maron et al. 1986 ]. This approach, however, often suffers a lack of compliance [ Ehrlich 2003 ]. As a result, much attention has been devoted to the automated acquisition of expertise information. Sources used include published documents such as resumes [ Becerra-Fernandez 2006 ]; Wikipedia content, discussions, and user data [ Demartini 2007 ]; literature databases [ Friedman et al. 2000 ]; newsgroup postings [ Terveen et al. 1997 ], and online community site data [ Bojars et al. 2008 ].

A key limitation of these approaches is that they conflate an individual’s credentials, expertise, and interests, each playing a different role in the evaluation of a potential collaborator. Credentials project an image of general competence in a domain, such as medicine or law. Expertise specifies knowledge and prior experience in one or more topics in that domain. Statements of interest provide information about current motivations. Collaboration seekers typically examine all three areas when assessing potential matches. For instance, a researcher’s publications provide a historical record of performance which is only useful in the context of current interests. If current interests do not match those of the collaboration seeker, even a highly productive publication record is irrelevant.

Derivation, representation, and presentation of potential collaborators’ expertise and interests are critical to the design of effective, sustainable RNSs. In light of this, we propose the following research questions.

• —How should type and extent of expertise and interests be represented to help researchers make nuanced and valid collaboration decisions?
• —Can researcher interests be inferred computationally or do they have to be specified by the user? Should both methods be used together?
• —How can the representation of a researcher’s expertise and research interests be kept up-to-date with minimal user effort? To what degree can current activity be inferred computationally, for instance, through the semantic Web [ Schleyer et al. 2008b ]? How should current and past activities be summarized and displayed to support identification and evaluation of collaboration potential?

RNS must represent individuals’ expertise, interests, and activities using controlled terminologies.

Some fields, such as biomedicine, have a strong tradition of using controlled terminologies [ Coletti and Bleich 2001 ]. Others, such as computer science, do not. Folksonomies [ Woolwine et al. 2011 ] have multiple advantages and benefits for indexing documents and people, including their authentic use of language and multiple potential interpretations. However, they also create problems for representing concepts in ways that are commonly understood [ Peters and Stock 2007 ]. Two approaches have been proposed to address the limitations of user-created tags. One is to improve users’ “tag literacy” [ Guy and Tonkin 2006 ], while the other considers tags as natural language elements amenable to automatic NLP methods [ Stock 2007 ].

A recent study by Lee and Schleyer [2012] found minimal overlap between social tags and controlled index terms for a sample of 231,388 biomedical research papers. A resulting challenge for RNSs is how to balance the effects of controlled and user-generated terminologies. Controlled terminologies are valuable because they provide high-quality information about a potential collaborator. They enable cross-disciplinary searches, support identification of synonyms and related terms, and facilitate automatic discovery of otherwise undetected similarities between individuals. Yet, controlled vocabularies necessarily place constraints on individuals’ ability to describe their expertise, interests, experiences, and characteristics in their own terms. To the degree that research networking is a process of impression management and impression formation, use of controlled vocabularies may be perceived by RNS users and subjects as unnecessarily limiting. The potentially conflicting implications of controlled vocabularies in the context of RNS suggest the following research questions.

• —Are existing controlled terminologies and taxonomies for indexing publications, such as the Medical Subject Headings [ Coletti and Bleich 2001 ] and the ACM Computing Classification System, adequate for representing individuals’ expertise, interests, and characteristics? If not, how should they be improved or expanded?
• —How should expertise and interests be represented in domains which lack widely accepted controlled terminologies?
• —What are the strengths of folksonomies and social tagging for representation of individual researchers? When and how should controlled and user-generated terms be combined in researcher profiles?
• —How does use of controlled terminologies affect individuals’ willingness to use, create and maintain profiles within an RNS?

RNSs must allow users to search and visualize researcher profiles in multiple ways.

RNSs are designed, in part, to make the large search spaces of potential collaborators tractable and accessible. A tension exists between focused result sets, in which the system provides a few, presumably high-quality, matches and broader ones, which require more user effort to explore. McDonald’s work [ McDonald 2003 ] suggests that RNSs should allow user experimentation and adaptation of the system for different purposes.

Previous work suggests that allowing users to apply different types of criteria may be beneficial. The Expertise Recommender [ McDonald and Ackerman 2000 ] offers “Departmental” and “Social Network” as filters for system recommendations. The Small-Blue system implements a social-context-aware expertise search system that presents an unfiltered list of experts with information about the degree of separation, allowing the user to select the “right” person using social connection information [ Ehrlich et al. 2007 ].

RNSs must incorporate and combine traditional methods of locating collaborators, such as social networks and expertise database searches. RNSs which treat collaborator identification as a decontextualized search process based on impersonal expertise profiles are unlikely to have much impact on users’ relationship formation and maintenance activities. Yet, RNSs which only reveal opportunities in the user’s immediate social context will overlook potentially fruitful chances for novel and interesting relationships. Taken together, these issues suggest the following research questions regarding the need for diverse presentation and discovery strategies in RNSs.

• —What different strategies should RNSs support for locating collaborators? When are strategies based on information artifacts, general profiles, and/or existing network structures most effective?
• —What types of filters and representation are most useful to users when navigating the research collaboration search space?
• —Should the presentation of RNS information depend on user characteristics, project features, or disciplinary norms? What are the primary dimensions that can be varied to create high-impact, individualized representations?
• —Which search algorithms minimize the user effort required to search efficiently and effectively for collaboration opportunities?

RNSs must balance the tension between seekers’ need for comprehensive information and potential collaborators’ desire to control how they are seen by others.

A collaboration seeker’s desire for comprehensive information needs to be balanced with potential collaborators’ requirements for privacy and access control [ DiMicco and Millen 2007 ; Hewitt and Forte 2006 ]. Privacy is not as central in expertise location systems as it is in RNSs [ Bellotti 1996 ; Fogel and Nehmad 2009 ] because expertise location focuses on task-oriented, episodic interactions. The long-term relationships that RNSs help establish, on the other hand, are central to an individual’s professional identity, career success, and self-efficacy. As a result, how an individual is presented to and seen by others in an RNS is an important factor [ Goffman 1959 ; Leary 1996 ; Schlenker 2003 ; Schlenker and Leary 1982 ].

Being visible and accessible in an RNS also carries different costs depending on individual characteristics. To some, the benefits of greater visibility outweigh any potential costs [ Gross et al. 2005 ]. Others may find the loss of privacy and control unacceptable [ Mann 2007 ; Rosenblum 2007 ]. A senior scientist with many existing collaborations may want to be less visible than a junior scientist for whom exposure can be advantageous. Thus, availability of privacy and access controls may be critical for an individual’s willingness to participate in an RNS.

Taken together, these issues suggest a fundamental tension in RNS design. For individuals seeking to form collaborations, the value of an RNS increases if it can provide comprehensive information about potential collaborators. However, the individuals being profiled may be wary of a public presentation of their expertise, interests, past activities, and personal characteristics that encourage detailed comparisons with others. Effective RNSs must balance the needs of both collaboration seekers and potential collaborators. This requirement suggests the following questions.

• —How does a researcher’s willingness to share different types of profile information vary and what are implications for RNS design? For instance, while researchers are unlikely to object to sharing public information, under what conditions will they be willing to share information about current research projects?
• —How do individuals react when information from many public sources about them is presented in one place?
• —How does the willingness to share information vary with the personal, social, and organizational distance to others? For instance, are researchers more or less willing to share information with other researchers in their home discipline?
• —How should RNS allow researchers to control privacy and public availability of information about themselves? How much control is reasonable without reducing the system’s utility?

RNSs should support serendipitous discovery of collaborative opportunities.

While query-driven interfaces play an important role in supporting research networking, effective RNSs must also promote appropriate serendipitous discovery. Like successful entrepreneurs [ Gaglio and Winter 2009 ], high-impact researchers are able to accomplish their goals in part because they can recognize and capitalize on emerging opportunities not obvious to them. Although deliberate planning and intentional search are an important part of forming collaborations, so too is the ability to identify and respond to unanticipated opportunities that emerge from the complex social, institutional, and intellectual environments in which research takes place. To fully support researchers’ efforts to form collaborative relationships, RNSs must facilitate both the intentional and serendipitous discovery of potential collaborators.

Matching services have been used with success in many social contexts, but it is less clear how they would be applied to research collaborations. The literature on social matching and collaborative support contains a number of algorithms to match potential partners [ Budzik et al. 2002 ; Pavlov and Ichise 2007 ; Terry et al. 2002 ; Zhang et al. 2007 ]. For example, Yenta is a distributed agent-based system that groups people with common interests by examining the content of their file systems [ Foner 1996 ]. MEDLINE Publications, a scientific collaboration tool built on Facebook, offers a recommendation engine that helps connect a user with others who have similar publication profiles, thereby exposing him to new potential collaborators [ Bedrick and Sittig 2008 ]. Active matching services, similar to the current awareness systems offered by many literature databases, might be set up to proactively notify users about potential collaboration opportunities. This RNS feature, if properly calibrated, would promote opportunistic formation of collaborative relationships.

Addressing the following questions could be useful in determining how RNSs can best facilitate serendipitous collaborations.

• —What algorithms are most useful for identifying potential collaboration partners? What variables should they take into account?
• —Should users be able to customize the recommendation and matching algorithms used in RNSs? What features/aspects of the matching process should be user-modifiable?
• —How can RNSs obtain and incorporate feedback about the usefulness of suggested matches [ Melin 2000 ]?
• —Can RNSs help identify the “gaps” in science which present significant research opportunities? How can results of conceptual gap analyses be combined with social network data, researcher profiles, and user characteristics to recommend meaningful novel collaboration opportunities?

The emergence of RNSs creates an opportunity for HCI researchers and developers to apply their understanding of presentation and user experience design to a problem domain that has previously only marginally been supported by technology. Novel aspects of research networking, such as presenting multidimensional researcher profiles, supporting boundary-crossing discovery, and balancing the often conflicting needs of searchers and subjects, present important design challenges. Addressing these challenges will advance our understanding of how to develop complex but usable interfaces can facilitate research networking.

3.3. Architecture

Although individual researchers have significant autonomy in determining the direction and nature of their collaborative efforts, research collaborations and the relationships that support them are solidly embedded in a web of social and institutional systems. Resources and individuals are associated with departments, labs, centers, and universities. Journals, conferences, and associations provide networking opportunities and outlets for work within specific disciplines. Corporate sponsors, government agencies, and private foundations provide resources and collect data about research activities. These overlapping institutions each have their own practices, procedures, formats, and systems for managing data, all of which place demands on researchers and affect efforts to form collaborations. To be effective, RNSs must account not only for the needs of the individual users, but also for the nature of the larger social and institutional contexts in which researchers work and live.

RNSs must integrate information from multiple systems, make use of meta-information such as indexing terms to synthesize the information, and present results in a cohesive manner.

Researchers produce many artifacts, including papers, abstracts, presentations, grant applications, Web pages, Internet postings, tools, methods, and datasets. These artifacts are stored in a variety of personal, local, regional, national, or global systems. Representing a researcher’s work comprehensively requires information from many different sources. For instance, information about a paper may reside on the author’s computer, an electronic journal Web site, and in MEDLINE, CiteSeer, and the Web of Science. Integrating data from heterogeneous sources is a significant challenge because few systems are designed to support machine-based information access or exchange.

RNSs must merge data about a person from several sources in the absence of a common identifier. One common, if mundane, example is retrieving an author’s publications unambiguously from MEDLINE [ Bedrick and Sittig 2008 ; McKibbon et al. 2002 ]. Queries for authors with common names result in many false positives which require additional processing or manual review. Similar problems on the Web have led to the emergence of semantic Web standards for data interchange and interoperation such as SIOC (Semantically Interlinked Online Communities) and FOAF (Friend-of-a-Friend) [ Bojars et al. 2008 ].

Once documents about a person have been retrieved, their content must be mean-ingfully integrated. Many domains lack the strong tradition of indexing information using controlled vocabularies that the National Library of Medicine has established in biomedicine [ Coletti and Bleich 2001 ]. Therefore, documents may be indexed using different controlled terminologies/ontologies or not at all. Various approaches have been proposed to solve this problem. Liu et al. [2005] proposed the Resource Description Framework (RDF) that combines semantically rich information with a domain ontology to facilitate integration. Cameron et al. [2007] showed how semantic annotation and FOAF can be used to determine the expertise of researchers across various areas of computer science. Jung et al.’s research [2007] discussed a method for finding topic-centric experts from open-access metadata and full text documents using OntoFrame, a semantic Web-based academic research information service. Other approaches to integrating information from multiple sources include ontology-based integration methods [ Wache et al. 2001 ], Digital Object Identifiers ( http://www.doi.org ), and persistent URL mechanisms ( http://purl.org ), MOMIS (Mediator envirOnment for Multiple Information Sources), a model of information integration based on the conceptual schema or metadata of the information sources [ Bergamaschi et al. 1999 ], and automated approaches to unifying heterogeneous information based on machine-processable meta-data specifications [ Singh 1998 ]. While these methods may be useful in particular contexts, the integration of large-scale classification systems and ontologies and, therefore, the information indexed by them, remains a fundamentally difficult problem [ Prevöt et al. 2005 ].

Being able to aggregate a scientist’s information artifacts does not mean that they can be easily synthesized into a comprehensive and coherent whole. The process is hindered because documents differ with respect to currency, validity, representation scheme, level of abstraction, audience, and focus. For instance, a list of recently published abstracts may be relatively current in representing a researcher’s interests. Nonetheless, it might not be valid if the researcher has abandoned some of the projects. Similarly, recent grants, abstracts, and papers drawn from a departmental Web site will only be useful as a source of current research interests if they can be correlated with the keyword terms that the individual has provided to describe his interests in other systems.

In addition to the technical problems of integration, RNS developers must also consider and address the social and organizational consequences of integration. Researchers are very conscious of the role that their work plays in the formation of their professional identity and reputation (see Claim 7 ). As a result, a composite profile drawing on data from multiple sources that is not under the control of the individual being profiled may create concern. This is further complicated if the technology incorporates information from systems that focus on informal or personal networking, such as Facebook and YouTube [ Bateman et al. 2011 ]. Balancing different perspectives of various information sources is critical if an RNSs are to be effective catalysts for collaborative partnerships.

This discussion suggests the following research questions about integration challenges faced by RNSs.

• —RNS that generate comprehensive profiles must acquire and integrate information from heterogeneous sources, such as CVs, MEDLINE, the NIH’s Reporter database, conference proceeding sites, online communities, and Web pages. How should RNSs interface with these sources and aggregate data about researchers?
• —How should different information artifacts about a researcher be synthesized? What attributes, such as currency, validity, representation scheme, level of abstraction, audience, and focus should be taken into account when creating comprehensive profiles?
• —Should data about researchers be managed in a central repository or using a federated approach, in which data are retrieved and synthesized on-the-fly? What issues and problems arise in managing data using either approach?
• —How should information content annotated with different types of meta-information, such as controlled vocabularies and social tags, be synthesized? How should information artifacts without meta-information be handled?
• —How does combining information from different spheres (e.g., personal and professional) affect the impressions that people form of one another?

RNS must integrate seamlessly with an individual’s workflow and the software applications that are part of it.

The scientific workflow in biomedical research and the software applications associated with it are a complex and challenging environment with which RNSs must be integrated. Researchers use a variety of tools, such as data management applications; general office applications, such as Microsoft Word and PowerPoint; reference databases, such as EndNote and CiteULike; conference and journal submission sites; and computer-supported cooperative work applications. Introducing RNSs that duplicate data entry, management, and reporting functions places unnecessarily burdens users and is likely to be met with resistance. Therefore, close integration with researchers’ existing workflows and practices is a key factor in facilitating the adoption of RNSs [ Schleyer et al. 2008a ].

In addition, RNSs must operate across organizational and disciplinary boundaries to be effective. Given the increasingly inter- and multidisciplinary nature of research, a researcher with several research interests is likely to join different communities that are independent, isolated, and supported by incompatible systems. The ability to easily bridge these systems is an essential part of facilitating cross-boundary collaborations. One attempt to solve this problem was introduced by Mitchell-Wong et al. [2007] in the OpenSocial framework. The DIRECT 7 project has also begun to interlink several major current research networking systems.

Research networking is simultaneously critical and secondary. Failure to collaborate undermines a researcher’s ability to complete many of the activities critical to successful scientific work. Hence, research networking activities are pervasive and important. At the same time, researchers do not develop collaborations for their own sake. In this sense, research networking is a secondary support activity. Successful RNS must balance these two concerns by supporting lightweight, low-impact integration between the networking system and the systems that are the primary tools of research. This suggests the following research questions regarding integration of RNS, other networking systems, and research workflow systems.

• —How should RNSs interface with each other and related systems, such as general social networking platforms? What standards for information exchange should be developed?
• —Researchers’ activities continuously produce artifacts and information that may be useful in RNS profiles. How can workflows for activities such as conducting experiments or writing a paper be leveraged to facilitate RNS profile maintenance?
• —How should RNSs integrate with other systems that researchers use in their work, both from a back-end and user interface perspective? For instance, RNSs could automatically populate an individual citation library in CiteULike or feed an expertise database for paper reviews.
• —How can RNSs help address the problem of duplicate information management requirements? For instance, academic and funding institutions require a variety of documents, performance reviews, and progress reports. How should RNS data be structured to facilitate sharing and reuse in other systems?

Research networking is an activity inherently tied to the institutional and social context. Researchers’ efforts to form and maintain collaborations are directly affected by the practices and systems around them. Successful RNSs must work with these existing systems, interconnected where the integration provides value and deliberately separate where they are able to improve on the existing capabilities. Hence, designing RNS architectures to allow for various forms of integration is essential to their ability to facilitate the formation of collaborations.

3.4. Evaluation

RNSs require buy-in from a range of stakeholders. Researchers must use the system, both maintaining their profile and searching for others. Administrators must provide the resources needed to implement RNSs, and support their integration with the systems and procedures of the local institutions. Each of these groups has different needs which may only be partially addressed by RNSs. Making the case for an RNS requires answering a range of fundamental questions about how it provides value for individuals, relationships, and organizations.

Evaluating RNS search results requires metrics which combine traditional information retrieval measures with those specific to collaboration.

Supporting collaboration seeking with an RNS requires that designers define criteria used to select candidates from the pool of available individuals. Although researchers often feel that selecting collaborators is idiosyncratic, context-specific, or even random, the capability to systematically evaluate individual profiles is critical in RNSs.

Evaluating RNSs for collaborator discovery in some ways parallels evaluating Information-Seeking Support Systems (ISSS) for Information Retrieval (IR). Models of information-seeking which can inform RNS design and evaluation include the five-stage information seeking process model [ Cole 1997 ], the Information Seeking Process [ Kuhlthau 1991 ], and the model of general information behavior [ Wilson 1999 ]. To evaluate ISSSs, Kelly et al. [2009] advocate the development of alternative user and task models, methods for assessing support of complex, evolving tasks, and longitudinal designs. As systems providing essential information to researchers to help them make decisions on potential collaborators, RNSs can be considered a type of ISSS. This suggests a need for RNS research which extends IR models to integrate models of the collaboration seeking processes, adds new evaluation methods and measures, and develops longitudinal designs with process-specific measures of learning, cognition, and engagement.

While traditional IR approaches provide a starting point for the social, relational, and instrumental aspects of collaborator discovery, critical differences between person discovery and document retrieval suggest that effective evaluation of RNSs will require fundamentally different approaches. One approach is to consider various frameworks for describing collaboration. For example, Larson [2003] identified three key components of collaboration: structure, process, and outcomes. Structure includes characteristics such as standardized methods of communicating, decision-making, and formal agreements for sharing data and other collaborative activities. Process is characterized by clear and explicit shared research goals and objectives, experience with the change process, strong and clear leadership, and efficient work procedures. Outcomes include measurable work products such as publications, dissertations, and presentations. Another framework more directly related to RNSs is the work of Kraut et al. [1987] .

Another critical aspect of RNS functionality is candidate ranking. In general, expertise location systems do not distinguish levels of expertise. Zhang et al. [2007] nonetheless have proposed an expertise-finding mechanism that can automatically infer expertise level from characteristics of postings in an online community. As a result, potential collaborators might be personalized to a candidate’s expertise level as well as to keyword similarity.

An overarching issue regarding searching in RNSs is what metrics should be used to assess the quality of the search. Measures typically used in information retrieval include recall and precision, but they require a gold standard against which they can be calculated. While it may be possible to identify a gold standard for RNS searches under narrowly scoped circumstances, such scenarios are not likely to fully reflect the range of concerns involved in forming collaborations.

RNSs depend on criteria for systematically evaluating and ranking potential collaborators to a user. As a result, the following research questions regarding candidate evaluation are central to the development of effective RNSs.

• —What model(s) of collaboration and information seeking are most appropriate and relevant to the evaluation of RNS results?
• —How should similarity and complementarity be incorporated into the metrics used by RNSs to evaluate potential collaborators? When should the similarity of two people be highly weighted? When should complementarity be emphasized?
• —What metrics are appropriate for assessing the outcome of a search for a collaborator using an RNS? Under what circumstances can IR metrics such as recall and precision be used?
• —How can process model(s) of collaboration formation inform the design of RNS evaluation metrics? For example, if we use Kraut et al.’s [1987] framework, potential questions include: What specific tasks are involved in forming a collaborative relationship? What strategies and tools do researchers use to complete each task? How does an RNS support the completion of these tasks?

Evaluation of RNSs must assess actual and perceived effects on individual users’ collaboration practices and outcomes.

In addition to evaluating the quality of potential collaborators identified by RNSs, it is necessary to assess the general effects of RNSs use on individual users. Such effects could include how individuals’ perceptions of RNS functionality and performance develop, and how these perceptions affect users’ decisions to participate as collaboration seekers, potential collaborators, or both.

Unlike traditional CSCW applications which focus on performance of tasks by members of well-defined teams, RNSs focus on facilitating a general class of social practices within a diverse, poorly defined community [ Neale et al. 2004 ]. While the general goal of RNSs is relatively clear, the particulars of how the goal is achieved, who is involved, when it is successfully achieved, and what constitutes successful use of the system are difficult to articulate. As a result, assessing RNS performance is highly complex, having more in common with evaluating medical decision support systems [ Friedman et al. 2006 ] than with evaluating traditional process-oriented applications. As with decision support systems, the evaluation of RNS faces challenges arising from crossing multiple research disciplines. As a result, to be useful for design improvement, assessment of RNSs must take into account a plethora of factors. Functional usability and perceived ease-of-use are likely important, but so too are questions of whether the system significantly impacts a researcher at various stages of a collaboration process, as well as long-term career advancement, research directions, and scientific impact.

While the primary goal of RNSs is to facilitate the formation of productive collaboration relationships, the outcome of these relationships is dependent on many other factors, including standardized communication modes, a highly efficient work process, and strong and clear leadership [ Larson 2003 ]. Given the difficulty of delineating the functional boundary between forming collaborations, maintaining the resulting relationships, and executing collaborative work tasks, it is impossible to evaluate the impact of RNSs in isolation. Therefore, it is important to define and assess variables at the various stages of collaboration that RNS may significantly impact.

Another consequence of the complexity of the collaboration formation process is that individual users will rarely have extensive, objective measures of systems performance on which to base their adoption and participation decisions. The presence of potentially conflicting user roles, that is, collaboration seeker and potential collaborator, means that past experience with the system may not be a clear indicator of future effort or outcomes. The extended timeframe of collaborative relationships and the presence of confounding factors also significantly limit an individual’s ability to accurately assess the correlation between use of a particular RNS and successful formation of a collaborative relationship. Consequently, user perceptions of system characteristics and impacts are likely to play a significant role in adoption decisions regardless of whether they are based on objective data or not. This suggests that the following questions regarding user perceptions and system assessment will be central to efforts to develop meaningful evaluations of RNSs.

• —What is a good collaboration decision? What are near-, medium- and long-term outcomes variables? Are individuals’ perceptions of desirable collaborative relationships consistent with those found in empirical studies [ Cummings and Kiesler 2008 ]?
• —How do individual users determine if an RNS is useful? What forms of evidence do they use to assess whether a networking system has significantly contributed to their efforts to form and maintain a collaborative relationship?
• —What indicators do users rely on to assess whether an RNS has enough participants to be worthwhile as a source of potential collaborators (i.e., critical mass)? How do users determine whether it is beneficial for them to maintain their profile in an RNS?
• —How do individuals assess the costs and benefits of using an RNS? What prior experiences provide the basis for expected costs and benefits? What features and outcomes are most salient in development of users’ overall assessment of the system?

Evaluation of RNSs must assess their impact on organizational and societal outcomes.

RNSs are infrastructure systems that can only prove their value through the effects they have on their users, the community and/or organization, and the scientific field(s) in which they are used. This raises question of who should invest in these systems and who will derive value from this investment.

The decision makers with the authority to allocate resources for development and maintenance of an RNS are typically not its target users. As a result, their view of the value and cost of an RNS is rarely the same as, or even consistent with, that of the individual users of the system. Where each user may consider the time and effort to maintain their profile a significant cost, an administrator may only see the cost of additional personnel needed to gather the information from external systems (treating researchers’ time as “free”). While a researcher might consider the system useful if it allows her to maintain her general awareness of activities taking place in her social network, a funder may seek more quantifiable outcomes such as cost reduction or increased volume of publications. Therefore, although user perceptions of RNSs are critical for its success, evaluation of the organizational- and societal-level impacts is also necessary for their success as sustainable infrastructure systems.

While RNSs and the associated collaborative relationships can be beneficial for researchers and institutions, they can also be costly. Katz and Martin [1997] describe the money, time, and increased administrative effort required to support cross-institutional collaborations. These costs must also be considered when assessing RNS impacts. Together these issues suggest the following questions regarding larger-scale outcomes of implementing RNSs.

• —How can the benefits of RNS deployment be quantified? Will there be significant cost reductions for organizations that implement RNSs or do they just shift work from one part of the organization to another? How can the outcomes of supporting collaboration formation be measured?
• —What is the appropriate timeframe for evaluation of RNSs? Is it reasonable to expect impacts of RNS use to be visible in months, years, or decades?
• —What is the relationship between RNS use and organizationally significant impact measures? Which outcomes supported by RNSs, such as increased research productivity and innovative projects, are most likely to result in significant cost reductions?
• —Under what conditions will introduction of RNSs have the greatest impact? What disciplines, areas, and populations will be most affected by the availability of RNSs?

4. CONCLUSION

Choosing appropriate collaborators in science is important and likely to become more so. As this review has shown, the HCI and CSCW literatures provide important background knowledge and foundational concepts for research on RNSs. Beyond core areas such as expertise location systems and virtual communities, advancing our knowledge of research networking must also draw on knowledge representation, ontologies/controlled terminologies, human-computer interaction, social network formation, social matching, and the semantic Web. Moving RNSs forward requires a broad but integrated research program.

Given the current state of RNS development, a rapid, iterative cycle between foundational research, design, implementation, and evaluation seems desirable. The major funding agencies for biomedical (NIH) and basic science (NSF) research in the U.S. are keenly interested in a rapid reengineering of the research enterprise towards a more collaborative approach [ Cummings et al. 2008 ]. CSCW and HCI are disciplines that can add tremendous value to this transformation.

A primary goal of this article is to stimulate the HCI, CSCW, and related communities to consider studying research networking systems. As such, we view our work as a starting point to motivate a much more expansive discussion of research networking systems, and the pursuit of a broad and comprehensive research agenda.

ACKNOWLEDGMENTS

We appreciate all reviewers’ thorough and thoughtful comments and suggestions, Ellen Detlefsen’s input, Janine Carlock’s copy edits, and Michael Dziabiak’s help with formatting and submission.

This project was, in part, supported by a grant (1 U54 RR023506-01) from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH) and NIH Roadmap for Medical Research. Addition funding was provided by the National Science Foundation (OCI-0951630).

Various parts of this article are built on discussions and findings in Spallek et al. [2008] and Schleyer et al. [2008a ; 2008b ].

1 See http://www.vivoweb.org

2 See http://www.kmdedge.org

3 http://www.vivoweb.org/

4 http://connects.catalyst.harvard.edu/Profiles/search

5 http://www.icts.uiowa.edu/Loki/

6 http://www.direct2experts.org

7 http://www.direct2experts.org

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New report says restricting social media access can help kids ... but only sometimes

A new report from the National Academies of Sciences, Engineering and Medicine released Wednesday grapples with the questions: Is social media harming teenagers? And what can parents, and the government, do about it? 

The answers are murky.

The authors surveyed hundreds of studies across more than a decade and came to complicated, occasionally contradictory, conclusions. 

On one hand, they found there isn’t enough population data to specifically blame social media for changes in adolescent health. On the other hand, as shown in study after study cited by the report, social media has the clear potential to hurt the health of teenagers, and in situations where a teenager is already experiencing difficulties like a mental health crisis, social media tends to make it worse. 

What is needed: more research and more coordination.

“There is much we still don’t know, but our report lays out a clear path forward for both pursuing the biggest unanswered questions about youth health and social media, and taking steps that can minimize the risk to young people using social media now,” Sandro Galea, dean of the Boston University School of Public Health and chair of the committee behind the report, said in a news release.

In adolescents, overly restrictive and controlling parental rules, like confiscating a phone for punishment, are often associated with that teenager taking more risks online.

“Our recommendations call on social media companies, Congress, federal agencies, and others to make changes that will protect and benefit young people who use social media,” he added.

Parents hoping for clear guidelines will have to keep waiting.

“The committee sympathizes with some parents’ desire for authoritative prescription on teenagers’ social media use but is also mindful of overreaching the data,” the report concludes. “Venturing hard and fast rules regarding teenagers’ use of social media, rules that the data cannot support, is not something this committee can do.”

The National Academies of Sciences, Engineering and Medicine is an advisory group tasked by Congress with providing guidance on science-related issues.

But its report suggests that parents are closer than ever to arriving at effective strategies for navigating their families through the social media landscape. In the future, calculating the harms and potential benefits of social media will have to take place on a case-by-case basis, it suggests, taking into account factors that will vary widely from teenager to teenager and family to family. 

For instance, the report says that while middle school girls have been found to experience social anxiety, body dissatisfaction and depression when they compared themselves with others on social media, factors such as media literacy, supportive parents and a positive school environment lessened those negative effects.  

The ways social media is used seem to make a difference. When a teenager passively scrolls, as opposed to actively posting, that’s connected by many studies to low life satisfaction and feelings of sadness. It may be that showcasing a hobby or an interest on social media doesn’t produce the same harms. 

But those rates differ by demographic group: Black, non-Hispanic participants in one study reported more negative moods during active social media use, suggesting that the potential benefits of posting on social media are not the same for teenagers of all backgrounds.

And age affects how well certain strategies work. In younger children (12 and under), a family policy that restricts social media except when it’s actively guided by a parent seems to reduce the risk of problematic use and inappropriate behavior online. But in adolescents (13 and older), overly restrictive and controlling parental rules, like confiscating a phone for punishment, are often associated with that teenager taking more risks online. 

“Restrictions on media use are useful for young children,” the authors write, “while increased communication and awareness are more suitable and helpful for teenagers.”

Faced with an urgent need to “create a more transparent industry and a better-informed consumer of social media,” the report calls on companies and regulators to establish international standards, such as clear ways for companies to share data with researchers and accepted best practices to avoid proven harms where possible. 

It recommends that the International Organization for Standardization — a body that sets global rules in areas such as manufacturing and food safety — be tasked with creating a new system, one that could be used by federal and international agencies to track and evaluate social media companies and the algorithms they build. And it asks for funding from the National Institutes of Health, the National Science Foundation and other agencies to pay for the sort of large, long-term studies that have in the past identified major public health crises. 

This story was first published on NBCNews.com.

Jacob Ward, a technology correspondent for NBC News, is a 2018-19 Berggruen Fellow at Stanford University’s Center for Advanced Study in the Behavioral Sciences, where he is writing a book about how artificial intelligence will shape human behavior. 

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Today's Climate

Fossil fuel funding is ‘embedded’ across academia. what does that mean for climate research, oil and gas companies often help fund climate research on campuses. but these ties could pose major—and often underreported—conflicts of interest, new research finds..

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In the most extensive analysis of its kind, new research suggests that fossil fuel influence is widespread across universities in the United States, United Kingdom, Canada and Australia. 

Oil and gas companies have poured funding into campuses for decades. But scientists, journalists and students are only just starting to uncover the true extent of these financial ties—and how potential conflicts of interest in higher education could hinder efforts to combat climate change, the study’s authors say. 

“It’s a really troubling lack of transparency that kind of has created this situation where people have been trying to pull back the curtain on some of this, but struggling because a lot of this data just is not in the public domain,” study co-author Geoffrey Supran , an associate professor of environmental science and policy at the University of Miami, told me. “We observe that fossil fuel companies have embedded themselves widely within universities.” 

In the past few years, student activists have increasingly pushed their universities to divest from oil and gas on campus and in investment portfolios. Now, this movement is trickling into the university research community amid a growing push to increase transparency of fossil fuel funding sources—and potentially cut ties altogether. 

Fossil Fuel Funding: Supran has firsthand experience with fossil fuel money permeating the research space. The first year of his doctoral studies at the Massachusetts Institute of Technology was funded by an oil company. 

“They took us to fancy Italian banquets, they gave us free stationery with their logos on, they funded the first year of my Ph.D. And so my only association with them was positive,” Supran said. He explained that this type of treatment could result in reciprocity bias, which is when someone may feel the expectation to return favors after receiving gifts or incentives.

“It wasn’t until I started to pay more attention to the oil industry’s political machinations that I started to open my eyes,” he said. 

MIT did not respond to a request for comment about how the university mitigates this type of bias. 

Supran noted that “conflicts of interest are not necessarily implied bias.” However, a 2022 study published in the journal Nature Climate Change found university research centers funded by fossil fuel companies were more supportive of natural gas than those that are not. 

The new study finds a dearth of research investigating other potential ways that fossil fuel funding can influence climate research. As part of their work, the scientists parsed through around 14,000 peer-reviewed articles about conflicts of interest, bias and research funding across all industries. Just seven discussed fossil fuels. 

But their own analysis of literature, news reports and other sources revealed hundreds of instances of fossil fuel ties on campuses—from industry representatives sitting on governing research boards to fossil fuel-sponsored scholarships, internships and field trips for students. Some experts argue that these types of university partnerships could help fossil fuel companies “greenwash” their image. 

In other cases, oil and gas companies could have outsize control over what types of climate research occurs, such as ExxonMobil’s influence on carbon capture projects at Louisiana State University, which The Guardian and The Lens reported on in April . 

In March, my colleague Phil McKenna wrote about a new climate change initiative at MIT’s Sloan School of Management, and some of his sources noted their concern that the school could seek future funding from fossil fuel companies, as it has before with other projects. MIT’s Energy Initiative, a separate research center dedicated to developing low-carbon solutions, has raised more than $1 billion for energy research since 2006, approximately 45 percent from oil and gas companies, a spokesperson for the MIT Energy Initiative told Inside Climate News. 

Other fields have faced similar scrutiny from the public for industry ties, particularly in the biomedical and tobacco sectors. 

Shining Light on Financial Ties: With greenhouse gas emissions continuing to rise, students across many campuses are demanding that their universities drop all direct investments in fossil fuels. Since this movement began, more than 200 educational institutions have pledged to divest, including New York University and Dartmouth College. 

Now, there is a call to action from experts in the climate space to impose policies that ban fossil fuel influence on university research, which University of California, San Diego’s Craig Callender calls “divestment 2.0.” 

“Before, it was divesting the portfolio of the university. Now it’s looking at all these entanglements throughout the university and wanting to disassociate in this way as well,” Callender, who studies ethics and philosophy in science and was not involved in the new research, told me. “This [new study] proves beyond a shadow of a doubt that this knowledge institution is being weaponized against the public good.”

In 2022, Callender wrote an op-ed for The Chronicle of Higher Education about how fossil-fuel funding is influencing university research articles in favor of oil and gas. Academic studies are often cited in efforts to enact energy policies in government. More than 750 academics signed a letter in 2022 pushing for a ban on fossil-fuel funding for climate research. 

However, pulling this funding could have widespread consequences for the universities that rely on it. Over the past decade, state governments have invested significantly less money on research at public colleges and universities than in the past, forcing many institutions to find the money elsewhere. Instead of a full-scale ban, some schools, such as UC San Diego, are pursuing policies that require public disclosure of all external funding, including from oil and gas. 

However, Supran said these efforts aren’t happening fast enough.

“We also have observed a kind of worrying delay that has occurred between when civil society initially began to raise the alarm about this problem in the early 2000s until when scholars, and especially university leaders, have begun to pay attention to this issue,” he said. 

More Top Climate News 

The stats are in: This summer was the hottest on record in the Northern hemisphere, according to the European Union’s Copernicus Climate Change Service. The average global temperature over the past three months was 1.24 degrees Fahrenheit hotter than the 1991-2020 average. There was also a revolving door of scorching extreme weather events and catastrophes—from devastating heat waves in Europe to fires that burned through California. 

In May, the National Oceanic and Atmospheric Administration predicted above-normal hurricane activity this season. But the past three weeks have been unusually quiet on the hurricane front in the Atlantic —in what is typically the throes of this season. This has scientists wondering if the forecast was wrong or if we are in for a late season blitz over the next month, Judson Jones reports for The New York Times .

Attacked by Yemen’s Houthi rebels, a burning oil tanker is idling in the Red Sea—and emergency workers ditched an initial effort to tow it away due to poor conditions , Jon Gambrell reports for The Associated Press . This could represent a looming ecological disaster: Experts say more damage on the boat could trigger one of the worst oil spills in recent history. 

“The onus is on the Houthis, again, to look at the impact that they’re having, not only in the short term, but on the long term as it relates to the environment, the economy and the safety of those that are transiting this important waterway,” U.S. Air Force Maj. Gen. Pat Ryder, the Pentagon’s press secretary, said in a statement . 

On a visit to Istiqlal Mosque in Jakarta, Pope Francis issued a joint statement with Grand Imam Nasaruddin Umar calling on Muslims and Catholics to push for “decisive action” in the face of climate change . 

“The human exploitation of creation, our common home, has contributed to climate change, leading to various destructive consequences such as natural disasters, global warming and unpredictable weather patterns,” the statement reads. The people of Jakarta are intimately familiar with the impacts of climate change as the city is quite literally sinking into the ocean while simultaneously being swallowed by rising sea levels.

As winters get hotter with climate change, ski resorts are hoarding stockpiles of snow to use during the peak season , Chris Baraniuk writes for Wired . To do this, owners are stashing the icy mounds under insulating blanket systems developed by companies that say the products can prevent melting even during hot summer days.  

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She earned her master’s degree in science journalism at New York University, and her bachelor’s degree in biology at Wake Forest University. Her work has appeared in National Geographic, Time, Scientific American and more. She is a former Pulitzer Reporting Fellow, during which she spent a month in Thailand covering the intersection between Buddhism and the country’s environmental movement.

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How Leaders Create and Use Networks

by Herminia Ibarra and Mark Lee Hunter

Summary .   

Reprint: R0701C

Most people acknowledge that networking—creating a fabric of personal contacts to provide support, feedback, insight, and resources—is an essential activity for an ambitious manager. Indeed, it’s a requirement even for those focused simply on doing their current jobs well. For some, this is a distasteful reality. Working through networks, they believe, means relying on “who you know” rather than “what you know”—a hypocritical, possibly unethical, way to get things done. But even people who understand that networking is a legitimate and necessary part of their jobs can be discouraged by the payoff—because they are doing it in too limited a fashion.

On the basis of a close study of 30 emerging leaders, the authors outline three distinct forms of networking. Operational networking is geared toward doing one’s assigned tasks more effectively. It involves cultivating stronger relationships with colleagues whose membership in the network is clear; their roles define them as stakeholders. Personal networking engages kindred spirits from outside an organization in an individual’s efforts to learn and find opportunities for personal advancement. Strategic networking puts the tools of networking in the service of business goals. At this level, a manager creates the kind of network that will help uncover and capitalize on new opportunities for the company. The ability to move to this level of networking turns out to be a key test of leadership.

Companies often recognize that networks are valuable, and they create explicit programs to support them. But typically these programs facilitate only operational networking. Likewise, industry associations provide formal contexts for personal networking. The unfortunate effect is to give managers the impression that they know how to network and are doing so sufficiently. A sidebar notes the implication for companies’ leadership development initiatives: that teaching strategic networking skills will serve their aspiring leaders and their business goals well.

When Henrik Balmer became the production manager and a board member of a newly bought-out cosmetics firm, improving his network was the last thing on his mind. The main problem he faced was time: Where would he find the hours to guide his team through a major upgrade of the production process and then think about strategic issues like expanding the business? The only way he could carve out time and still get home to his family at a decent hour was to lock himself—literally—in his office. Meanwhile, there were day-to-day issues to resolve, like a recurring conflict with his sales director over custom orders that compromised production efficiency. Networking, which Henrik defined as the unpleasant task of trading favors with strangers, was a luxury he could not afford. But when a new acquisition was presented at a board meeting without his input, he abruptly realized he was out of the loop—not just inside the company, but outside, too—at a moment when his future in the company was at stake.

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