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Social Network Analysis 101: Ultimate Guide

Comprehensive introduction for beginners.

Social network analysis is a powerful tool for visualizing, understanding, and harnessing the power of networks and relationships. At Visible Network Labs, we use our network science and mapping tools and expertise to track collaborative ecosystems and strengthen systems change initiatives. In this Comprehensive Guide, we’ll introduce key principles, theories, terms, and tools for practitioners framed around social impact, systems change, and community health improvement. Let’s dig in!

Learn more and get started with the tools below in our complete Guide.

Table of Contents

You can read this guide from start-to-finish or use the table of contents to fast forward to a topic or section of interest to you. The guide is yours to use as you see fit.

Introduction

Let’s start by reviewing the basics, like a definition, why SNA is important, and the history of the practice. If you want a quick intro to this methodology, download our Social Network Analysis Brief .

Definition of Social Network Analysis (SNA)

Social Network Analysis , or SNA, is a research method used to visualize and analyze relationships and connections between entities or individuals within a network. Imagine mapping the relationships between different departments in a corporation. The outcome would be a vivid picture of how each department interacts with others, allowing us to see communication patterns, influential entities, and bottlenecks

The Importance of SNA

SNA is a powerful tool. It allows us to explore the underlying structure of an organization or network, identifying the formal and informal relationships that drive the formal processes and outcomes. This insight can enable better communication, facilitate change management, and inspire more efficient collaboration.

This methodology also helps demonstrate the impact of relationship-building and systems change efforts by documenting the changes in the quality and quantity of relationships before and after the initiative. The maps and visualizations produced by SNA are an engaging way to share your progress and impact with stakeholders, donors, and the community at large.

Brief Historical Overview of SNA

The concept of SNA emerged in the 1930s within the field of sociology. Its roots, however, trace back to graph theory in mathematics. It was not until the advent of computers and digital data in the 1980s and 1990s that SNA became widely used, revealing new insights about organizational dynamics, community structures, and social phenomena.

While it originated as an academic research tool, it is increasingly used to inform real-world practice. Today, it is used in a broad variety of industries, fields, and sectors, including business, web development, public health, foundations and philanthropy , telecommunications, law enforcement, academia, and systems change initiatives, to name a few.

Fundamentals of SNA

SNA is a broad topic, but these are some of the essential terms, concepts, and theories you need to know to understand how it works.

Nodes and Edges

In SNA, nodes represent individuals or entities while edges symbolize the relationships between them. For example, in an inter-organizational network, nodes might be companies, and edges could represent communication, collaboration, or competition.

Network Types

Different types of networks serve different purposes. ‘Ego Networks’ focus on one node and its direct connections, revealing its immediate network. ‘Whole Networks’, on the other hand, capture a broader picture, encompassing an entire organization or system. Open networks are loosely connected, with many opportunities to build new connections, ideal for innovation and idea generation – while closed networks are densely interconnected, better for refining ideas amongst a group who all know each other.

Network Properties

Properties such as density (the proportion of potential connections that are actual connections), diameter (the longest distance between two nodes), and centrality (the importance of a node within the network) allow us to understand the network’s structure and function. Metrics also can measure relationship quality across the network, like our validated trust and value scores.

Dyadic and Triadic Relationships

Dyadic relationships involve two nodes, like a partnership between two companies. Triadic relationships, involving three nodes, are more complex but can offer richer insights. For instance, it might show how a third company influences the relationship between two others, or which members of your network are the best at building new relationships between their peers.

Homophily and Heterophily

Homophily refers to the tendency of similar nodes to connect, while heterophily is the opposite. In a business context, we might see homophily between companies in the same industry and heterophily when seeking diversity in a supply chain. Many networks aim to be diverse but get stuck talking to the same, similar partners. These network concepts underly many strategies promoting network innovation to avoid group-think among likeminded partners.

Network Topologies

Lastly, the layout or pattern of a network, its topology, can reveal much about its function. For instance, a centralized topology, where one node is connected to all others, may indicate a hierarchical organization, while a decentralized topology suggests a more collaborative and flexible environment. This is also referred to as the structure of the network. Read more.

Theoretical Background of SNA

Many different theories have developed to explain how certain network properties, like their topology, centrality, or type, lead to different outcomes. Here are several key theories relevant to SNA.

Strength of Weak Ties Theory

This theory postulates that weak ties or connections often provide more novel information and resources compared to strong ties. These “weak” relationships, which may seem less important, can serve as important bridges between different clusters within a network. Read more.

Structural Hole Theory

This theory posits that individuals who span the structural holes, or gaps, in a network—acting as a bridge between different groups—hold a strategic advantage. They can control and manipulate information and resources flowing between the groups, making their position more influential. Read more

Small World Network Theory

This theory emphasizes the interconnectedness of nodes within a network. It suggests that most nodes can be reached from any other node through a relatively short path of connections. This property leads to the famous phenomenon of “six degrees of separation,” indicating efficient information transfer and connectivity in a network.

Barabási–Albert (Scale-Free Network) Model

This model suggests that networks evolve over time through the process of preferential attachment, where new nodes are more likely to connect to already well-connected nodes. This results in “scale-free” networks, where a few nodes (“hubs”) have many connections while the majority of nodes have few.

Data Collection and Preparation

Every network mapping begins by collecting and preparing data before it can be analyzed. This data varies widely, but at a basic level, they must include data on nodes (the entities in the network) and data on edges (the lines between nodes representing a relationship or connection). Additional data on the attributes of the nodes or edges add more levels of analysis and insight but are not strictly necessary.

Primary Methods for Collecting SNA Data

This can be as simple as conducting interviews or surveys within an organization. The more complex the network, the more difficult it is to collect good primary data: If you have more than 5-10 partners, interviews and surveys are hard to conduct by hand.

Network survey tools like PARTNER collect relational data by asking respondents who they are connected to, and then asking them about aspects of their relationships to provide trust, value, and network structure scores. This is impossible to do using most survey software like Google Forms without hours of cleaning by hand.

Response rates are an important consideration if using surveys for data collection. Unlike a typical survey where a small sample is representative, a network survey requires a high response rate – 80% and above are considered the gold standard.

In an inter-organizational context where surveys are impossible, or you cannot achieve a valid response rate, one might gather data through business reports, contracts, or publicly available data on partnerships and affiliations. For example, you could visit an organization’s website to note who they list as a partner – and do the same for others – to generate a basic SNA map.

Secondary Sources of SNA Data

Secondary sources include data that was already collected but can be used again, often to complement your use of primary data you collect yourself. This might include academic databases, industry reports, or social media data. It’s important to ensure the accuracy and reliability of these sources.

You can also conduct interviews or focus groups with network members to add a qualitative perspective to your results. These mixed-method SNA projects provide a great deal more depth to their network maps through their conversations with numerous network representatives to explore deeper themes and perspectives.

Ethical Considerations in Data Collection

When collecting data, it’s crucial to ensure privacy, obtain necessary permissions, and anonymize data where necessary. Respecting these ethical boundaries is critical for maintaining trust and integrity in your work.

Consider also how your SNA results will be used. For example, network analysis can help assess how isolated an individual is to target them for interventions. Still, it could also be abused by insurance companies to charge these individuals a higher rate (loneliness increases your risk of death).

Lastly, consider ways to involve the communities with stake in your SNA using approaches like community-based participatory research. Bring in representatives from target populations to help co-design your initiative or innovation as partners, rather than patients or research subjects.

Preparing Data for Analysis

Data needs to be formatted correctly for analysis, often as adjacency matrices or edgelists. Depending on the size and complexity of your network, this can be a complex process but is crucial for meaningful analysis.

If you are new to SNA, you can start by laying out your data in tables. For example, the table below shows a relational data set for a set of partners within a public health coalition. The first column shows the survey respondent (Partner 1), the second shows who they reported as a partner, the third shows their reported level of trust, and the fourth their reported level of collaboration intensity. This is just one of many ways to lay out and organize network data.

Depending on which analysis tool you choose, a varying degree of data preparation and cleaning will be required. Usually, free tools require the most work, while software with subscriptions do a lot of it for you.

Partner 1	Partner 2	Trust (1-4)	Level of Collaboration
Mayor’s Office	Local Hospital	3	Coordination
Public Health Dept.	Primary Care Clinic	4	Cooperation
Mayor’s Office	Public Health Dept.	2	Awareness

Network Analysis Methods & Techniques

There are many ways to analyze a network or set of entities using SNA. Here are some of basic and advanced techniques, along with info on network visualization – a major component and common output of SNA projects.

Basic Technique: Network Centrality

One of the most common ways to analyze a network is to look at the centrality of various nodes to identify key players, information hubs, and gatekeepers across the network. There are three types of centrality, each corresponding to a different aspect of connectivity and centrality. Degree, Betweenness, and Closeness Centrality are measures of a node’s importance.

Degree Centrality  

Can be used to identify the most connected actors in the network. These actors are considered “popular” or “active” and they often have a strong influence within the network due to their numerous direct connections. In a coalition or network, these nodes could be the organizations or individuals that are most active in participating or the most engaged in the network activities. They may be the ‘go-to’ people for information or resources and have a significant impact on shaping the group’s agenda.

Betweenness Centrality

A useful for identifying the “brokers” or “gatekeepers” in the network. These actors have a unique position where they connect different parts of the network, facilitating or controlling the flow of information between others. In a coalition context, these could be the organizations or individuals who have influence over how information, resources, or support flow within the network, by virtue of their position between other key actors. These actors could play crucial roles in collaboration, negotiation, and conflict resolution within the network.

Closeness Centrality

A measure of how quickly a node can reach every other node in the network via the shortest paths. In a coalition, these nodes can disseminate information or exert influence quickly due to their close proximity to all other nodes. These ‘efficient connectors’ are beneficial for the rapid spread of information, resources, or innovations across the network. They could play a vital role during times of rapid change or when swift collective action is required.

Advanced Techniques: Clusters and Equivalence

Clustering Coefficients

The Clustering Coefficient provides insights into the “cliquishness” or local cohesion of the network around specific nodes. In a coalition or inter-organizational network, a high clustering coefficient may indicate that a node’s connections are also directly connected to each other, forming tight-knit groups or sub-communities within the larger network. These groups often share common interests or objectives, and they might collaborate or share resources more intensively. Understanding these clusters can be crucial for coalition management as it can highlight potential subgroups that may need to be engaged differently, or that might possess different levels of influence or commitment to the coalition’s overarching goals.

Structural Equivalence

Structural Equivalence is used to identify nodes that have similar patterns of connections, even if they do not share a direct link. In a coalition context, structurally equivalent organizations or individuals often occupy similar roles or positions within the network, and thus may have similar interests, influence, or responsibilities. They may be competing or collaborating entities within the same sectors or areas of work. Understanding structural equivalence can provide insights into the dynamics of the network, such as potential redundancies, competition, or opportunities for collaboration. It can also reveal how changes in one part of the network may impact other, structurally equivalent parts of the network.

Visualizing Networks

Network visualization is a key tool in Social Network Analysis (SNA) that allows researchers and stakeholders to see the ‘big picture’ of the network structure, as well as discern patterns and details that may not be immediately evident from numerical data. Here are some key aspects and benefits of network visualization in the context of a coalition or inter-organizational network:

Overview of Network Structure: Visualizations provide a snapshot of the entire network structure, including nodes (individuals or organizations) and edges (relationships or interactions). This helps to comprehend the overall size, density, and complexity of the network. Seeing these relationships mapped out can often make the network’s structure more tangible and easier to understand.

Identification of Key Actors: Centrality measures can be represented visually, making it easier to identify key actors or organizations within the network. High degree nodes, gatekeepers, and efficient connectors will stand out visually, which can assist in identifying who holds influence or power within the network.

Detecting Subgroups and Communities: Visualization can also highlight clusters or subgroups within the network. These might be based on shared interests, common goals, or frequent interaction. Understanding these subgroups is crucial for coalition management and strategic planning, as different groups might have unique needs, concerns, or levels of engagement.

Identifying Outliers and Peripheral Nodes: Network visualizations can also help in identifying outliers or peripheral nodes – those who are less engaged or connected within the network. These actors might represent opportunities for further engagement or potential risks for network cohesion.

Highlighting Network Dynamics: Visualizations can be used to show changes in the network over time, such as the formation or dissolution of ties, the entry or exit of nodes, or changes in nodes’ centrality. These dynamics can provide valuable insights into the evolution of the coalition or network and the impact of various interventions or events.

Software and Tools for SNA

SNA software helps you collect, clean, analyze, and visualize network data to simplify the process of of analyzing social networks. Some tools are free with limited functionality and support, while others require a subscription but are easier to use and come with support. Here are some popular s tools used across many application

Introduction to Popular SNA Tools

Tools like UCINet, Gephi, and Pajek are popular for SNA. They offer a variety of functions for analyzing and visualizing networks, accommodating users of varying skill levels. Here are ten tools for use in different contexts and applications.

• UCINet: A comprehensive software package for the analysis of social network data as well as other 1-mode and 2-mode data.
• NetDraw: A tool usually used in tandem with UCINet to visualize networks.
• Gephi: An open-source network analysis and visualization software package written in Java.
• NodeXL: A free and open-source network analysis and visualization software package for Microsoft Excel.
• Kumu: A powerful visualization platform for mapping systems and better understanding relationships.
• Pajek: Software for analysis and visualization of large networks, it’s particularly good for handling large network datasets.
• SocNetV (Social Networks Visualizer): A user-friendly, free and open-source tool.
• Cytoscape: A bioinformatics software platform for visualizing molecular interaction networks.
• Graph-tool: An efficient Python module for manipulation and statistical analysis of graphs.
• Polinode: Tools for network analysis, both for analyzing your own network data and for collecting new network data.

Choosing the Right Tool for Your Analysis:

The right tool depends on your needs. For beginners, a user-friendly interface might be a priority, while experienced analysts may prefer more advanced functions. The size and complexity of your network, as well as your budget, are also important considerations.

PARTNER CPRM: A Community Partner Relationship Management System for Network Mapping

For example, we created PARTNER CPRM, a Community Partner Relationship Management System, to replace the CRMs used by most organizations to manage their relationships with their network of strategic partners. Incorporating data collecting, analysis, and visualization features alongside CRM tools like contact management and email tracking, the result is a powerful and easy-to-use network mapping tool.

SNA Case Studies

Looking for a real-world example of a social network analysis project? Here are three examples from recent projects here at Visible Network Labs.

Case Study 1: Leveraging SNA for Program Evaluation

SNA is increasingly becoming a vital tool for program evaluation across various sectors including public health, psychology, early childhood, education, and philanthropy. Its potency is particularly pronounced in initiatives centered around network-building.

Take for instance the Networks for School Improvement Portfolio by the Gates Foundation. The Foundation employed PARTNER, an SNA tool, to assess the growth and development of their educator communities over time. The SNA revealed robust networks that offer valuable benefits to members by fostering information exchange and relationship development. By repeating the SNA process at different stages, they could verify their ongoing success and evaluate the effectiveness of their actions and adjustments.

Read the Complete Case Study Here

Case Study 2: Empowering Coalition-building

In the realm of policy change, building a coalition of partners who share a common goal can be pivotal in overturning the status quo. SNA serves as a strategic tool for developing a coalition structure and optimizing pre-existing relationships among the members.

The Fix CRUS Coalition in Colorado, formulated in response to the closure of five major peaks to public access, is a prime example of this. With the aim of strengthening state liability protections for landowners, the coalition employed PARTNER to evaluate their network and identify key players. Their future plans involve mapping connections to important legislators as their bill progresses through the state legislature. Additionally, their network maps and reports will prove instrumental in acquiring grants and funding.

Case Study 3: Boosting Employee Engagement

In the private sector, businesses are increasingly harnessing SNA to optimize their employee networks, both formal and informal, with the goal of enhancing engagement, productivity, and morale.

Consider the case of Acuity Insurance. In response to a transition to a Hybrid-model amid the COVID-19 pandemic, the company started using PARTNER to gather network data from their employees. Their aim was to maintain their organizational culture and keep employee engagement intact despite the model change. Their ongoing SNA will reveal the level of connectedness within their team, identify employees who are over-networked (and hence at risk of burnout), and pinpoint those who are under-networked and could be missing crucial information or opportunities.

Read More About the Project Here

Challenges and Future Directions in Network Analysis

Like all fields and practices, social network analysis faces certain limitations. Practitioners are constantly innovating to find better ways to conduct projects. Here are some barriers in the field and current trends and predictions about the future of SNA.

The Limitations of SNA

SNA is a powerful tool, but it’s not without limitations. It can be time-consuming and complex, particularly with larger networks. Response rates are important to ensure accuracy, which makes data collection more difficult and time-consuming. SNA also requires quality, validated data, and the interpretation of results can be subjective. Software that helps to address these problems requires a significant investment, but the results are often worth it.

Lastly, SNA is a skill that takes time and effort to learn. If you do not have someone in-house with network analysis skills, you may need to hire someone to carry out the analysis or spend time training an employee to build the capacity internally.

Current Trends and Future Predictions

One emerging trend is the increased application of SNA in mapping inter-organizational networks such as strategic partnerships, community health ecosystems, or policy change coalitions. Organizations are realizing the power of these networks and using SNA to navigate them more strategically. With SNA, they can identify key players, assess the strength of relationships, and strategize on how to optimize their network for maximum benefit.

In line with the rise of data science, another trend is the integration of advanced analytics and machine learning with SNA. This fusion allows for the prediction of network behaviors, identification of influential nodes, and discovery of previously unnoticed patterns, significantly boosting the value derived from network data.

The future of SNA is likely to see a greater emphasis on dynamic networks – those that change and evolve over time. With increasingly sophisticated tools and methods, analysts will be better equipped to track network changes and adapt strategies accordingly.

In addition, there is a growing focus on inter-organizational network resilience. As global challenges such as pandemics and climate change underscore the need for collaborative solutions, understanding how these networks can withstand shocks and adapt becomes crucial. SNA will play an instrumental role in identifying weak spots and strengthening the resilience of these networks.

Conclusion: Social Network Analysis 101

SNA offers a unique way to visualize and analyze relationships within a network, be it within an organization or between organizations. It provides valuable insights that can enhance communication, improve efficiency, and inform strategic decisions.

This guide provides an overview of SNA, but there is much more to learn. Whether you’re interested in the theoretical underpinnings, advanced techniques, or the latest developments, we encourage you to delve deeper into this fascinating field.

Resources and Further Reading

For those who want to build more SNA skills and learn more about network science, check out these recommendations for further reading and exploration from the Visible Network Labs team of network science experts.

Recommended Books on SNA

• “Network Science” by Albert-László Barabási – A comprehensive introduction to the theory and applications of network science from a leading expert in the field.
• “Analyzing Social Networks” by Steve Borgatti, Martin Everett, and Jeffrey Johnson – An accessible introduction, complete with software instructions for carrying out analyses.
• “Social Network Analysis: Methods and Applications” by Stanley Wasserman and Katherine Faust – A more advanced, methodological book for those interested in a deep dive into the methods of SNA.
• “Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives” by Nicholas Christakis and James Fowler – An engaging exploration of how social networks influence everything from our health to our political views.
• “The Network Imperative: How to Survive and Grow in the Age of Digital Business Models” by Barry Libert, Megan Beck, and Jerry Wind – An excellent book for those interested in applying network science in a business context.
• “Networks, Crowds, and Markets: Reasoning About a Highly Connected World” by David Easley and Jon Kleinberg – An interdisciplinary approach to understanding networks in social and economic systems. This book combines graph theory, game theory, and market models.

Online Resources and Courses

Here are some online learning opportunities, including online courses, communities, resources hubs, and other places to learn about social network analysis.

• Social Network Analysis  by Lada Adamic from the University of Michigan
• Social and Economic Networks: Models and Analysis  by Matthew O. Jackson from Stanford University
• Introduction to Social Network Analysis  by Dr. Jennifer Golbeck from the University of Maryland, College Park
• Statistics.com :   Statistics.com offers a free online course called  Introduction to SNA  taught by Dr. Jennifer Golbeck.
• The Social Network Analysis Network:  This website provides a directory of resources on network methods, including courses, books, articles, and software.
• The SNA Society:  This organization provides a forum for social network analysts to share ideas and collaborate on research. They also offer a number of resources on their website, including a list of online courses.

Journals and Research Papers on SNA

These are a few of the most influential cornerstone research papers in network science and analysis methods:

• “The Strength of Weak Ties” by Mark Granovetter (1973)
• “Structural Holes and Good Ideas” by Ronald Burt (2004)
• “ Collective dynamics of ‘small-world’ networks” by Duncan Watts & Steven Strogatz (1998)
• “The structure and function of complex networks.” by M.E. Newman (2003).
• “Emergence of scaling in random networks” by A. Barabasi (1999).

Check out these peer-reviewed journals for lots of network science content and information:

• Social Networks : This is an interdisciplinary and international quarterly journal dedicated to the development and application of network analysis.
• Network Science : A cross-disciplinary journal providing a unified platform for both theorists and practitioners working on network-centric problems.
• Journal of Social Structure (JoSS) : An electronic journal dedicated to the publication of network analysis research and theory.
• Connections : Published by the International Network for Social Network Analysis (INSNA), this journal covers a wide range of social network topics.
• Journal of Complex Networks : This journal covers theoretical and computational aspects of complex networks across diverse fields, including sociology.

Frequently Asked Questions about SNA

A: SNA is a research method used to visualize and analyze relationships and connections within a network. In an organizational context, SNA can be used to explore the structure and dynamics of an organization, such as the informal connections that drive formal processes. It can reveal patterns of communication, identify influential entities, and detect potential bottlenecks or gaps.

A: The primary purpose of SNA is to uncover and visualize the relationships between entities within a network. By doing so, it allows us to understand the network’s structure and dynamics. This insight can inform strategic decision-making, facilitate change management, and enhance overall efficiency within an organization.

A: SNA allows researchers to examine the relationships between entities, the overall structure of the network, and the roles and importance of individual entities within it. This can involve studying patterns of communication, collaboration, competition, or any other type of relationship that exists within the network.

A: SNA has a wide range of applications across various fields. In business, it’s used to analyze organizational structures, supply chains, and market dynamics. In public health, it can map the spread of diseases. In sociology and anthropology, SNA is used to study social structures and relationships. Online, SNA is used to study social media dynamics and digital marketing strategies.

A: Key concepts in SNA include nodes (entities) and edges (relationships), network properties like density and centrality, and theories such as the Strength of Weak Ties and Structural Hole Theory. It also encompasses concepts like homophily and heterophily, which describe the tendency for similar or dissimilar nodes to connect.

A: An example of SNA could be a study of communication within a corporation. By treating departments as nodes and communication channels as edges, analysts could visualize the communication network, identify key players, detect potential bottlenecks, and suggest improvements.

A: Social Network Analysis refers to the method of studying the relationships and interactions between entities within a network. It involves mapping out these relationships and applying various analytical techniques to understand the structure, dynamics, and implications of the network.

A: In psychology, SNA can be used to study the social relationships between individuals or groups. It might be used to understand the spread of information, the formation of social groups, the dynamics of social influence, or the impact of social networks on individual behavior and well-being.

A: SNA can be conducted at different levels, depending on the focus of the study. The individual level focuses on a single node and its direct connections (ego networks). The dyadic level looks at the relationship between pairs of nodes, while the triadic level involves three nodes. The global level (whole network) considers the entire network.

A: There are several types of networks in SNA, including ego networks (focused on a single node), dyadic and triadic networks (focused on pairs or trios of nodes), and whole networks. Networks can also be categorized by their structure (like centralized or decentralized), by the type of relationships they represent, or by their application domain (such as organizational, social, or online networks).

A: SNA is used to visualize and analyze the relationships within a network. Its insights can inform strategic decisions, identify influential entities, detect potential weaknesses or vulnerabilities, and enhance the efficiency of communication or processes within an organization or system. It’s also an essential tool for research in fields like sociology, anthropology, business, public health, and digital marketing.

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Social Network Analysis: How to Get Started

Social network analysis is a great way to analyze relationships. Here’s how to build your own network graph.

Networks are all around us  —  road networks, internet networks and online social networks like Facebook. While this article focuses on social network analysis (SNA), these techniques will give you valuable tools to gain insight on a variety of data sources.

In order to build SNA graphs, we need two key components: actors and relationships. We commonly use SNA techniques with the internet. Web pages often link to other sites  —  either on their own website or an external page. These links can be considered relationships between actors (web pages) and this is a key component of search engine architecture.

What Is Social Network Analysis?

What does a social network graph look like.

A social network graph contains both points and lines connecting those points  —  similar to a connect-the-dot puzzle. The points represent the actors and the lines represent the relationships. An example of a social network graph would be this one, which demonstrates  community detection of ISIS Twitter accounts .

More From Mitch Ditch Your Passwords — They’re Only Hurting You

What Tools Do I Need to Get Started?

Like many things in data science, there are a variety of tools you can use to conduct SNA. This guide focuses on a specific set of tools in order to get you started making network graphs and conducting analysis on them. In no way are these the only or best tools available.

We’ll use Gephi , a free software for Mac, PC, and Linux, in order to build network graphs and run some analytics on them. Gephi provides a GUI interface (seen below) and will not require any coding. 

Python/Excel

In order to build network graphs in Gephi, we’ll need to use a specific data format and we’ll need to fit our data in the correct format (CSV files). With simple data, Excel should suffice. However, when using large amounts of data or data that must have its relationships extracted, I recommend Python. Don’t fret if you don’t have any Python skills — you should still be able to build basic networks.

Data Source

You’ll also need a data source for your network. Network data have two requirements: actors and relationships. Some data will require these relationships to be extracted, and others will be more explicit. I recommend using data sets from Kaggle to get started, such as:

Marvel Universe Social Network

Wikipedia Article Network

Deezer Social Network

Defining Our Terms

Nodes and edges.

Up until now, I’ve referred to both actors and relationships. In network science, actors are referred to as nodes (the dots on the graph) and relationships as edges (the lines on the graph).

Nodes can represent a variety of actors. For example, in internet networks nodes can represent web pages while in social networks nodes can represent people. While nodes can represent a variety of things, each node always has a relationship with another thing.

Edges can represent a variety of relationships. In internet networks, edges can represent hyperlinks and in social networks edges can represent connections. Nodes and edges are a key concept in networks, so make sure you have a good understanding of them before tackling the other concepts.

Edge Direction

There are two types of edges: directed and undirected. It will be necessary to decipher what type of edge your data contains when building a network graph. 

Directed edges are applied from one node to another with a starting node and an ending node. For example, when a Twitter user tags another Twitter user in a tweet, that relationship is directed. The user who wrote the tweet (starting node) applied that relationship to the user who they tagged (ending node). The tagged user has not necessarily reciprocated that relationship. Another example of a directed edge are payments. If a customer (starting node) pays a coffee shop (ending node) for a coffee, that relationship is not necessarily reciprocated because the coffee shop has not also paid the customer.

Undirected edges are the opposite of directed edges. These relationships are reciprocated by both parties without a clear starting node or ending node. For example, if two people are friends on Facebook, that relationship is undirected. This is because person A is friends with person B, but we can also say person B is friends with person A.

Edge Weight

An edge’s weight is the number of times that edge appears between two specific nodes. For example, if person A buys a coffee from a coffee shop three times, the edge connecting person A and the coffee shop will have a weight of three. However, if person B only buys coffee from the coffee shop once, the edge connecting person B and the coffee shop will have a weight of one.

Centrality Measures

Centrality is a collection of metrics used to quantify how important and influential a specific node is to the network as a whole. It’s important to remember that centrality measures are used on specific nodes within the network, and don’t provide information on a network level. There are several centrality measures, but this guide will cover degree, closeness and betweenness.

A node’s degree is the number of edges the node has. In an undirected network, there’s only one measure for degree. For example, if node A has edges connecting it to node B and node D, then node A’s degree is two.

However, in a directed network, there are actually three different degree measures. Because these edges have a starting and end node, the in-degree (number of edges the node is an end node of), out-degree (number of edges a node is a starting node of), and degree (number of edges a node is either a starting node or end node of) can be calculated.

Closeness measures how well connected a node is to every other node in the network. A node’s closeness is the average number of hops required to reach every other node in the network. A hop is the path of an edge from one node to another. For example, node A is connected to node B, and node B is connected to node C. For node A to reach node C it would take two hops.

Betweenness

Betweenness measures the importance of a node’s connections in allowing nodes to reach other nodes (in a hop). A node’s betweenness is the number of shortest paths the node is included in divided by the total number of shortest paths. This will provide the percentage of shortest paths in the node’s network.

Network-Level Measures

We can also calculate metrics on the network level to evaluate the entire network instead of merely a single node. Like centrality measures, there are a variety of network-level measures. We’ll look at size and density.

Network Size

Network size is the number of nodes in the network. The size of a network does not take into consideration the number of edges. For example, a network with nodes A, B, and C has a size of three.

Network Density

Network density is the number of edges divided by the total possible edges. For example, a network with node A connected to node B, and node B connected to node C, the network density is 2/3 because there are two edges out of a possible three.

Path-Level Measures

Path-level measures provide information for a path between one node and another node. Paths follow edges between nodes, known as hops. There are also many different path-level measures, but we’ll look at length and distance.

Length is the number of edges between the starting and ending nodes, known as hops. We must predetermine a path in order to calculate the length between two nodes. 

Distance is the number of edges or hops between the starting and ending nodes following the shortest path. Unlike length, the distance between two nodes uses only the shortest path  —  the path that requires the least hops.

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Connected Components and Bridges

Not all nodes in a network will necessarily be connected to each other. A connected component is a group of nodes that are connected to each other, but not connected to another group of nodes. Another way of thinking of this is a group of connected nodes that have no path to a node from another group. Depending on the network, there can be many connected components, or even only one. The diagram below shows a network with two connected components.

A bridge is a node that when removed, creates a connected component. Another way of thinking about it is that a bridge is a node that is the sole connection of a group of connected nodes to another group of connected nodes.

Hubs and Authorities

Hubs and Authorities are node classifications used in directed networks. A hub is a node that has many edges pointing out of it. You can also think of a hub as a node that’s the starting node of many edges. An authority, on the other hand, is a node that has many edges pointing to it. You can also think of authority as a node that is the ending node of many edges. There’s not a pre-determined number of edges that makes a node a hub or an authority; it will depend on the network. In addition, remember that not all nodes in a directed network will be a hub or an authority.

Dyads and Cliques

Dyads and cliques are pairings of nodes connected by edges. A dyad is a pairing of two nodes, while a clique is a pairing of three or more nodes. While a dyad or clique may be a connected component, they can also be part of a larger connected component.

Implementation

Now that you have an understanding of social network analysis terms and concepts, this guide will walk you through applying these techniques to a data set using Gephi

Download and Install Gephi

First, download and install the Gephi software for the operating system your machine is running. Gephi is available for Mac, PC, and Linux .

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For this guide, we’ll be using the Marvel Universe Social Network data set from Kaggle . While this data set is already laid out with a node and edge list, when working with data sets not structured as a network this will require some data transformation skills. I recommend using Python and Pandas in these situations.

After downloading the data set, there will be three csv files: nodes, edges, and network. Open the file nodes.csv in Excel.

The nodes file contains a list of all the nodes in the network. This file has two columns: node and type. This network contains two different types of nodes that represent different actor types: heroes and comics.

There is no data preparation needed to import this node list into Gephi, so we’ll close the file.

Next, open the file edges.csv in Excel.

The edges file also contains two columns: hero and comic. Each row in this table represents a single edge. The hero node and comic node are the two nodes connected by the edge.

In Gephi, an edges table requires the column headers of “source” and “target.” In an undirected network it doesn’t matter which node is in which column. However, in a directed network the source column contains the starting node and the target column contains the ending node. Rename column A to “source” and column B to “target.” Then save the file.

Loading Network Data into Gephi

Now that the node and edge lists are properly formatted for Gephi, it is time to load the data.

Open the Gephi software. It should look like this:

Click on new project. If you don’t see the welcome screen, go to file>new project.

Then, click the data laboratory tab.  

The data laboratory tab is where we’ll load in our edge and node list files. To import a list click the import spreadsheet button.

Then navigate to the folder containing the data sets and open the nodes file.

An import wizard will then walk you through correctly importing the node list. Set separator to comma, import as to nodes table, and charset as UTF-8. Then click next.

After clicking next, the wizard will provide additional setting configurations. Set time representation to intervals. For imported columns, check the node and type boxes and set their data types to string. Then, click finish.

There is one more step in importing the nodes list. Set graph type to undirected and edges merge strategy to sum. Ensure that it’s set up to append to the existing workspace. Then, click OK.

You should now see some data in the data laboratory window! Next we need to import the edges list.

Now that you’ve imported the  data it’s s time to view the graph. Click on the overview tab.

Using Layout Functions

You might be disappointed in the graph that was visualized. It will likely look like the black mess below.

In order to make the graph more readable, we’ll need to use a layout function to change the position of nodes in the graph.

There are a variety of layout functions in Gephi but let’s focus on the ForceAtlas 2 function for now. Select this function and then click run. You’ll see the nodes move in real-time, and you can stop the function when you like the nodes’ position.

After running the layout function your graph should look something like the one below. You can continue to play with other layout functions if you wish to get a better node position. While we’re using the stock ForceAtlas 2 parameters, changing them can give you better control over the node positions. In addition, you can change the parameters of layout functions.

Calculating Network-Level Measures

Now let’s calculate  the network size and density of this Marvel network.

The network size is easy to find. In the upper right-hand corner is a pane called context. This window provides the number of nodes and edges in the graph. Because a network’s size is the number of nodes in it, the network size of our Marvel network is 19,090.

To find the network density, we’ll take our first dive into the statistics window. Click on the statistics tab.

This is what you should see:

The statistics window contains many measures that can be calculated on the network. To find the network density, click run for graph density.

Select undirected, and then click OK.

A new window will pop up showing the results. This Marvel network has a density of 0.001.

You can save this report by clicking the save button in the bottom left-hand corner, or close it by clicking the close button in the bottom right-hand corner.

Calculating Centrality Measures

Recall that centrality measures are on a node-level, and not a network-level. However, we can also average centrality measures to get a network-level metric. In Gephi, you calculate centrality measures as a network-level average, which then also inputs the centrality measure on a node-level into the data laboratory tab.

Node Degree

To calculate node degree, click run on the average degree algorithm in the statistics window.

The report will provide you with the average degree for the network, as well as a distribution graph. While these can be useful in some applications, we are more interested in the degree on a node-level. Close the report.

To see the degree for each node in the network, go back to the data laboratory window and click on the node table. You will see a new column in the data titled degree.

Node Closeness and Betweenness

Calculating node closeness and betweenness is a similar process as calculating node degree. In the statistics window, click run on the network diameter algorithm.

Select undirected and click OK. Depending on the specs of your machine this may take a little while to calculate.

Like with the node measure, Gephi will provide a network-level report. Click close on this report and go to the data laboratory.

In the data laboratory, you’ll find additional columns in the node table including the node betweenness and closeness.

Calculating Edge Weights

Edge weights are auto-calculated in Gephi. You can find them in the edge list within the data laboratory.

Using Color in Network Graphs

Currently, our graph nodes and edges are black, providing no additional information. You can color-code both nodes and edges in Gephi. The coloring options are in the appearance window.

To color-code the nodes of the graph based on the node degree, click on the nodes button and the color palette button in the appearance window.

There are three options to encode information in the color of nodes: unique, partition and ranking. If you want to change all the graph’s nodes to the same color, use the unique window. Partition will break the nodes into color-coded groups. Ranking will color-code the nodes on a scale.

Let's color the nodes by their degree. To do this, click on the ranking section and select degree.

A color scale will be used to color the nodes. To select a new scale, click on the color selector button to the right of the color scale.

You can select any color scale to use. Then click apply.

As you can see in the above image, coloring our nodes also colored our edges. You can change the color of edges to a specific color using the unique color tab for edges, or apply a ranking or partitioning color scale to them.

Using Size in Network Graphs

You may also notice that the majority of the graph is colored red. This is because most nodes in the graph have a low degree. Zooming in will show that some nodes are yellow or blue.

To make these nodes easier to see in the graph let’s scale the size of the nodes to the node degree as well. To do this click on the nodes and size buttons in the appearance window.

Then, click on ranking and select degree. Change the minimum size to 1 and the maximum size to 100.

Then click apply. We can now better see what nodes have a high degree.

Changing Background Color

Let's also change the background from white to black. Depending on the colors in a graph, either color may look better and it’s often up to personal preference. To change the color to black, press the lightbulb button.

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Putting It All Together

This should get you started as you make your first network graph using the Marvel data set and I encourage you to continue playing around with this graph in Gephi. There are many more measures you can calculate and other appearances you can use.

Your next step should be to take another data set and try to reproduce these steps. Eventually, you can try to collect your own data and transform it into a new social network data for analysis.

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Social Network Analysis. Christopher McCarty University of Florida. Books. Social Network Analysis: A Handbook by John Scott, London: Sage (2000). Social Network Analysis: Methods and Applications . Stanley Wasserman and Katherine Faust. Cambridge: Cambridge University Press (1994).

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Social Network Analysis Christopher McCarty University of Florida

Books • Social Network Analysis: A Handbook by John Scott, London: Sage (2000). • Social Network Analysis: Methods and Applications. Stanley Wasserman and Katherine Faust. Cambridge: Cambridge University Press (1994). • Social Networks and Health: Models, Methods and Applications by Tom Valente (2010) Oxford: Oxford University Press. • Understanding Social Networks: Theories, Concepts and Findings (2011) Charles Kadushin’s Oxford: Oxford University Pres. • The Development of Social Network Analysis: A Study in the Sociology of Science Linton C. Freeman, Empirical Press, Vancouver, BC (2004). • The SAGE Handbook of Social Network Analysis (2011) Eds. John Scott and Peter Carrington. London: Sage Publications

Web Sites • www.insna.org -- International Network for Social Network Analysis • http://faculty.ucr.edu/~hanneman/nettext/ -- Tutorial for UCINET/Netdraw • http://www.redes-sociales.net/ (Spanish social network listserv)

Software • Ucinet(Whole networks) • (www.analytictech.com) ($40 for students, $150 for faculty) • E-net (Batch processing of ego networks) • (www.analytictech.com) • Pajek (Whole networks, large networks) • (http://vlado.fmf.uni-lj.si/pub/networks/pajek/) • Egonet(Personal networks) • (http://sourceforge.net/projects/egonet/) • Vennmaker (Personal networks) • Siena (Network modeling, longitudinal) • http://stat.gamma.rug.nl/siena.html • C-IKNOW(Online network data collection) • http://ciknow.northwestern.edu/ • ORA (Whole network analysis) • http://www.casos.cs.cmu.edu/projects/ora/ • Visone(Whole and Personal network analysis) • http://visone.info/ • Statnet • http://csde.washington.edu/statnet/

Journals • Social Networks • Connections • Journal of Social Structure • American Journal of Sociology, Social Science and Medicine, Journal of Mathematical Sociology, Organization Science, Social Forces, Gerontologist

Sunbelt Conference • 2001 – Budapest, Hungary – April 25-29 • 2002 – New Orleans, LA – March 1-9 • 2003 – Cancun, Mexico – March 1-9 • 2004 – Portoroz, Slovenia – May12-16 • 2005 – Redonda Beach, CA – February 16-20 • 2006 – Vancouver, Canada – April 25-30 • 2007 – Corfu, Greece – May 1-6 • 2008 – St. Pete Beach, FL – January 22-27 • 2009 – San Diego, CA – March 10-15 • 2010 – Riva gel Garda, Italy – June 29-July 4 • 2011 – St. Pete Beach, FL – February 8-13 • 2012 – Redonda Beach, CA – March 12-18 • 2013 – Hamburg, Germany – May 21-26

Social Network Analysis is the study of the pattern of interaction between actors

Examples of actors and their networks • Children in a preschool • Employees in an office • Customers of AT&T mobile phone service • NGOs working in the Amazon • Companies in the Fortune 500 • Countries in the European Union • Baboons in a troupe • Organisms in the Chesapeake Bay • Web sites around the world

Is SNA just a set of tools or is it a theoretical approach?See: http://www.insna.org/PDF/Sunbelt/3_KeynotePDF.pdf • Social Capital, Structural Holes, Simmelian ties • Strong and weak ties • Small world • Scale-free networks • Network diffusion

Social Capital • “…the ability of actors to secure benefits by virtue of membership in social networks or other social structures” (Portes 1998) • Alejandro Portes (1998) SOCIAL CAPITAL: Its Origins and Applications in Modern Sociology, Annual Review of Sociology 1998. 24:1–24 • Ron Burt (2004) Structural Holes and Good Ideas, American Journal of Sociology 110: 349–399

David Krackhardt (1999) The ties that torture: Simmelian tie analysis in organizations, Research in the Sociology of Organizations 16: 183-210

Structure matters (but is not always enough) • In some contexts structure is a necessary, but not sufficient, condition for social capital • The most beneficial structural position may depend on the topic • Job seeking • Social support • Social network evaluation and intervention does not always mean you should connect the dots • Facebook model is to suggest connections • Sometimes there are reasons for not connecting

Strong and weak ties • The most beneficial tie may not always be the strong ones • Strong ties are often connected to each other and are therefore sources of redundant information • Mark Granovetter (1973) The strength of weak ties American Journal of Sociology 78-1361-1381.

Small world phenomenon • Being linked, seemingly by chance, through someone via a friend or acquaintance • Stanley Milgram (1967)The Small World Psychology Today 2:60–67. • Peter D. Killworth, H. Russell Bernard and Christopher McCarty (1984) Measuring Patterns of Acquaintance Current Anthropology 25:381-397 • Duncan Watts and Steven Strogatz (1998) Collective dynamics of 'small-world' networks Nature 393 (6684): 409–10

Scale Free Networks • Scale free refers to the power law structure of networks as the number of actors increases • Networks tend to form hubs • Entry of physicists into SNA • Albert-LászlóBarabási and Réka Albert (1999) Emergence of scaling in random network. Science, 286:509-512.

Network Diffusion • Network structures can often aid or impede the flow of information and the adoption of innovations • Diffusion of innovation is the basis for peer to peer network interventions • Coleman, James, Elihu Katz, and Herbert Menzel. 1957. The diffusion of innovation among physicians. Sociometry. 20:253-270. • Valente, Thomas W. 1996 “Social network thresholds in the diffusion of innovations” Social Networks 18:69-89. • Klovdahl, A. S. (1985). Social networks and the spread of infectious diseases: The AIDS example. Social Science Medicine, 21(11), 1203-1216.

Whole (Complete, Sociocentric) Network Analysis Focus on interaction within a group Boundary defines social space Collect data from members of a group about their ties to other group members Personal (Egocentric) Network Analysis Focus on effects of network on individual attitudes, behaviors and conditions Use attributes of personal network to represent social context Collect data from respondent (ego) about interactions with network members (alters) Two kinds of Social Network Analysis

Sociocentric Network Data From Graduate Anthropology Course

Three network components • Beth is most degree central • Amber is most between central • Thomas and Kent are structurally equivalent • Removal of David maximizes network fragmentation

Boundary definition • Boundaries can be defined: • Geographically (a village) • Socially (an organization) • Through connections (snowball) • The idea is that actors within the boundary are in some way affected by their social position • This excludes the effects from those outside the boundary

Missing data • In whole networks responses by others about non-respondents can capture structure • 70% will in many cases be enough • GueorgiKossinets (2006) Effects of missing data in social networks. Social Networks 28: 247–268. • Costenbader, E. & Valente, T. W. (2004). The stability of centrality when networks are sampled. Social Networks.

Personal network interview • Identify a population • Select a sample of respondents • Ask questions about respondent • Elicit network members • Ask questions about each network member • Ask respondent to evaluate ties between network members

Personal Network CompositionAlter summary file

Personal network composition variables • Proportion of personal network that are women • Average age of network alters • Proportion of strong ties • Average number of years knowing alters

Personal Network StructureAlter adjacency matrix

Personal network structural variables • Average degree centrality (density) • Average closeness centrality • Average betweenness centrality • Core/periphery • Number of components • Number of isolates

Boundary definition for personal networks • Facebook • West Africa and Asia • Time • First grade teacher • Require mutual recognition • Book author • Living • Dead relative (Genogram) • Jesus

Two categories of data collection • One mode data • Actors by actors • Examples of one mode data collection • Survey; E-mail; Telephone calls; Observation of interaction • Two mode data • Actors by events • Examples of two mode data collection • Attendance at parties, meetings, funerals; Purchase of items; Reading particular authors

Kinds of data

Example 1One mode - Whole network

University – Water Management District interaction • Objective: Understand structure of interaction between academic and applied scientists • Procedure 1: Bound universities by those published in journals in St. Johns Water Management District library in 2008 • Procedure 2: Bound WMD by employee e-mails on web sites • Procedure 3: Web survey with letter and $1 incentive to all 705 actors • Response: 332 completed surveys

Visualization of University-WMD network

Example 2One mode - Personal network

Acculturation study • Objective: Test social network compositional and structural variables as proxies for acculturation • Procedure 1: Interviewed 535 migrants in Barcelona and New York City • Procedure 2: Each respondent listed 45 network alters • Procedure 3: Respondents provided twelve pieces of information about each alter • Procedure 4: Respondents evaluated all 990 unique alter-alter ties

Visualization of the networks of two sistersLabel = Country of origin, Size = Closeness, Color = Skin color, Shape = Smoking Status • Mercedes is a 19-year-old second generation Gambian woman in Barcelona • She is Muslim and lives with her parents and 8 brothers and sisters • She goes to school, works and stays home caring for her siblings. She does not smoke or drink. • Laura is a 22-year-old second generation Gambian woman in Barcelona • She is Muslim and lives with her parents and 8 brothers and sisters • She works, but does not like to stay home. She smokes and drinks and goes to parties on weekends.

Example 3Two mode - Whole network

Southern women • Objective: To understand the network structure of the debutante network in a Southern town in the 1940s • Procedure: Observe which of the 14 annual balls each of the 18 women attended

Two mode data matrix

Visualization of two mode data

Example 4Two mode - personal network

Relation categories in Thailand • Objective: Discover mutually exclusive and exhaustive categories in a language for how people know each other to be used on a network scale-up survey instrument

Procedure 1: Twenty one respondents freelist in Thai ways that people know each other

Procedure 2: Twenty one respondents list 30 people they know and apply 26 most frequently occurring categories

Affiliation from all respondents

Graph of relationship between knowing categories

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Course Status :	Completed
Course Type :	Elective
Duration :	12 weeks
Category :
Credit Points :	3
Undergraduate/Postgraduate
Start Date :	25 Jul 2022
End Date :	14 Oct 2022
Enrollment Ends :	08 Aug 2022
Exam Date :	29 Oct 2022 IST