Social network analysis
Social network analysis (SNA) is a strategy for investigating social structures through the use of network and graph theories. It characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties or edges (relationships or interactions) that connect them. Examples of social structures commonly visualized through social network analysis include social media networks, friendship and acquaintance networks, kinship, disease transmission,and sexual relationships. These networks are often visualized through sociograms in which nodes are represented as points and ties are represented as lines.
Social network analysis has emerged as a key technique in modern sociology. It has also gained a significant following in anthropology, biology, communication studies, economics, geography, history, information science, organizational studies, political science, social psychology, development studies, and sociolinguistics and is now commonly available as a consumer tool.
Social network analysis has its theoretical roots in the work of early sociologists such as Georg Simmel and Émile Durkheim, who wrote about the importance of studying patterns of relationships that connect social actors. Social scientists have used the concept of "social networks" since early in the 20th century to connote complex sets of relationships between members of social systems at all scales, from interpersonal to international. In the 1930s Jacob Moreno and Helen Jennings introduced basic analytical methods. In 1954, J. A. Barnes started using the term systematically to denote patterns of ties, encompassing concepts traditionally used by the public and those used by social scientists: bounded groups (e.g., tribes, families) and social categories (e.g., gender, ethnicity). Scholars such as Ronald Burt, Kathleen Carley, Mark Granovetter, David Krackhardt, Edward Laumann, Anatol Rapoport, Barry Wellman, Douglas R. White, and Harrison White expanded the use of systematic social network analysis. Even in the study of literature, network analysis has been applied by Anheier, Gerhards and Romo, Wouter De Nooy, and Burgert Senekal. Indeed, social network analysis has found applications in various academic disciplines, as well as practical applications such as countering money laundering and terrorism.
Homophily: The extent to which actors form ties with similar versus dissimilar others. Similarity can be defined by gender, race, age, occupation, educational achievement, status, values or any other salient characteristic. Homophily is also referred to as assortativity.
Multiplexity: The number of content-forms contained in a tie. For example, two people who are friends and also work together would have a multiplexity of 2. Multiplexity has been associated with relationship strength.
Mutuality/Reciprocity: The extent to which two actors reciprocate each other’s friendship or other interaction.
Network Closure: A measure of the completeness of relational triads. An individual’s assumption of network closure (i.e. that their friends are also friends) is called transitivity. Transitivity is an outcome of the individual or situational trait of Need for Cognitive Closure.
Bridge: An individual whose weak ties fill a structural hole, providing the only link between two individuals or clusters. It also includes the shortest route when a longer one is unfeasible due to a high risk of message distortion or delivery failure.
Centrality: Centrality refers to a group of metrics that aim to quantify the "importance" or "influence" (in a variety of senses) of a particular node (or group) within a network. Examples of common methods of measuring "centrality" include betweenness centrality, closeness centrality, eigenvector centrality, alpha centrality and degree centrality.
Structural holes: The absence of ties between two parts of a network. Finding and exploiting a structural hole can give an entrepreneur a competitive advantage. This concept was developed by sociologist Ronald Burt, and is sometimes referred to as an alternate conception of social capital.
Tie Strength: Defined by the linear combination of time, emotional intensity, intimacy and reciprocity (i.e. mutuality). Strong ties are associated with homophily, propinquity and transitivity, while weak ties are associated with bridges.
Groups are identified as ‘cliques’ if every individual is directly tied to every other individual, ‘social circles’ if there is less stringency of direct contact, which is imprecise, or as structurally cohesive blocks if precision is wanted.
Cohesion: The degree to which actors are connected directly to each other by cohesive bonds. Structural cohesion refers to the minimum number of members who, if removed from a group, would disconnect the group.
Modelling and visualization of networks
Visual representation of social networks is important to understand the network data and convey the result of the analysis . Numerous methods of visualization for data produced by Social Network Analysis have been presented. Many of the analytic software have modules for network visualization. Exploration of the data is done through displaying nodes and ties in various layouts, and attributing colors, size and other advanced properties to nodes. Visual representations of networks may be a powerful method for conveying complex information, but care should be taken in interpreting node and graph properties from visual displays alone, as they may misrepresent structural properties better captured through quantitative analyses.
Collaboration graphs can be used to illustrate good and bad relationships between humans. A positive edge between two nodes denotes a positive relationship (friendship, alliance, dating) and a negative edge between two nodes denotes a negative relationship (hatred, anger). Signed social network graphs can be used to predict the future evolution of the graph. In signed social networks, there is the concept of "balanced" and "unbalanced" cycles. A balanced cycle is defined as a cycle where the product of all the signs are positive. Balanced graphs represent a group of people who are unlikely to change their opinions of the other people in the group. Unbalanced graphs represent a group of people who are very likely to change their opinions of the people in their group. For example, a group of 3 people (A, B, and C) where A and B have a positive relationship, B and C have a positive relationship, but C and A have a negative relationship is an unbalanced cycle. This group is very likely to morph into a balanced cycle, such as one where B only has a good relationship with A, and both A and B have a negative relationship with C. By using the concept of balanced and unbalanced cycles, the evolution of signed social network graphs can be predicted.
Especially when using social network analysis as a tool for facilitating change, different approaches of participatory network mapping have proven useful. Here participants / interviewers provide network data by actually mapping out the network (with pen and paper or digitally) during the data collection session. An example of a pen-and-paper network mapping approach, which also includes the collection of some actor attributes (perceived influence and goals of actors) is the * Net-map toolbox. One benefit of this approach is that it allows researchers to collect qualitative data and ask clarifying questions while the network data is collected.
Social network analysis is used extensively in a wide range of applications and disciplines. Some common network analysis applications include data aggregation and mining, network propagation modeling, network modeling and sampling, user attribute and behavior analysis, community-maintained resource support, location-based interaction analysis, social sharing and filtering, recommender systems development, and link prediction and entity resolution. In the private sector, businesses use social network analysis to support activities such as customer interaction and analysis, marketing, and business intelligence needs. Some public sector uses include development of leader engagement strategies, analysis of individual and group engagement and media use, and community-based problem solving.
Social network analysis is also used in intelligence, counter-intelligence and law enforcement activities. This technique allows the analysts to map a clandestine or covert organization such as a espionage ring, an organized crime family or a street gang. The National Security Agency (NSA) uses its clandestine mass electronic surveillance programs to generate the data needed to perform this type of analysis on terrorist cells and other networks deemed relevant to national security. The NSA looks up to three nodes deep during this network analysis. After the initial mapping of the social network is complete, analysis is performed to determine the structure of the network and determine, for example, the leaders within the network. This allows military or law enforcement assets to launch capture-or-kill decapitation attacks on the high-value targets in leadership positions to disrupt the functioning of the network.
Large textual corpora can be turned into networks and then analysed with the method of Social Network Analysis. In these networks, the nodes are Social Actors, and the links are Actions. The extraction of these networks can be automated, by using parsers.
The resulting networks, which can contain thousands of nodes, are then analysed by using tools from Network theory to identify the key actors, the key communities or parties, and general properties such as robustness or structural stability of the overall network, or centrality of certain nodes. This automates the approach introduced by Quantitative Narrative Analysis, whereby subject-verb-object triplets are identified with pairs of actors linked by an action, or pairs formed by actor-object.
- Actor-network theory
- Complex Network
- Community structure
- Dynamic network analysis
- Digital humanities
- Friendship paradox
- Graph theory
- Individual mobility
- Mathematical sociology
- Metcalfe's Law
- Network science
- Organizational patterns
- Small world phenomenon
- Social networking service
- Social network analysis software
- Social software
- Social Terrain
- Social web
- Net-map toolbox
- Social media mining
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- Facebook friends mapped by Wolfram Alpha app BBC News
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- Freeman, L. C. (2004). The development of social network analysis: a study in the sociology of science. Vancouver, B. C.: Empirical Press.
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- Anheier, H.K.; Gerhards, J.; Romo, F.P. (1995). "Forms of capital and social structure of fields: examining Bourdieu's social topography". American Journal of Sociology 100: 859–903. doi:10.1086/230603.
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- Hansen, Derek et al. (2010). Analyzing Social Media Networks with NodeXL. Morgan Kaufmann. p. 32. ISBN 978-0-12-382229-1.
- Liu, Bing (2011). Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer. p. 271. ISBN 978-3-642-19459-7.
- Hanneman, Robert A. & Riddle, Mark (2011). "Concepts and Measures for Basic Network Analysis". The Sage Handbook of Social Network Analysis. SAGE. pp. 364–367. ISBN 978-1-84787-395-8.
- Tsvetovat, Maksim & Kouznetsov, Alexander (2011). Social Network Analysis for Startups: Finding Connections on the Social Web. O'Reilly. p. 45. ISBN 978-1-4493-1762-1.
- The most comprehensive reference is: Wasserman, Stanley, & Faust, Katherine. (1994). Social Networks Analysis: Methods and Applications. Cambridge: Cambridge University Press. A short, clear basic summary is in Krebs, Valdis. (2000). "The Social Life of Routers." Internet Protocol Journal, 3 (December): 14–25.
- Opsahl, Tore; Agneessens, Filip; Skvoretz, John (2010). "Node centrality in weighted networks: Generalizing degree and shortest paths". Social Networks 32 (3): 245–251. doi:10.1016/j.socnet.2010.03.006.
- "Social Network Analysis". Field Manual 3-24: Counterinsurgency (PDF). Headquarters, Department of the Army. pp. B–11 – B–12.
- Xu, Guandong et al (2010). Web Mining and Social Networking: Techniques and Applications. Springer. p. 25. ISBN 978-1-4419-7734-2.
- Cohesive.blocking is the R program for computing structural cohesion according to the Moody-White (2003) algorithm. This wiki site provides numerous examples and a tutorial for use with R.
- Hanneman, Robert A. & Riddle, Mark (2011). "Concepts and Measures for Basic Network Analysis". The Sage Handbook of Social Network Analysis. SAGE. pp. 346–347. ISBN 978-1-84787-395-8.
- Moody, James, and Douglas R. White (2003). "Structural Cohesion and Embeddedness: A Hierarchical Concept of Social Groups." American Sociological Review 68(1):103–127. Online: (PDF file).
- Pattillo, Jeffrey et al (2011). "Clique relaxation models in social network analysis". In Thai, My T. & Pardalos, Panos M. Handbook of Optimization in Complex Networks: Communication and Social Networks. Springer. p. 149. ISBN 978-1-4614-0856-7.
- Hamdaqa, Mohammad; Tahvildari, Ladan and LaChapelle, Neil and Campbell, Brian (2014). "Cultural Scene Detection Using Reverse Louvain Optimization". Science of Computer Programming 95: 44–72. doi:10.1016/j.scico.2014.01.006.
- Bacher, R. (1995). Graphical Interaction and Visualization for the Analysis and Interpretation of Contingency Analysis Result. In Proceedings of the 1995 Power Industry Computer Applications (pp . 128-134) . Salt Lake City, USA: IEEE Power Engineering Society
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- McGrath, Blythe and Krackhardt. 1997. "The effect of spatial arrangement on judgements and errors in interpreting graphs". Social Networks 19: 223-242.
- Bernie Hogan, Juan-Antonio Carrasco and Barry Wellman, "Visualizing Personal Networks: Working with Participant-Aided Sociograms," Field Methods 19 (2), May 2007: 116-144.
- Golbeck, J. (2013). Analyzing the Social Web. Morgan Kaufmann, ISBN 0-12-405856-6>
- "NSA warned to rein in surveillance as agency reveals even greater scope". 17 July 2013. Retrieved 19 July 2013.
- "How The NSA Uses Social Network Analysis To Map Terrorist Networks". 12 June 2013. Retrieved 19 Jul 2013.
- "NSA Using Social Network Analysis". 12 May 2006. Retrieved 19 July 2013.
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- Automated analysis of the US presidential elections using Big Data and network analysis; S Sudhahar, GA Veltri, N Cristianini; Big Data & Society 2 (1), 1-28, 2015
- Network analysis of narrative content in large corpora; S Sudhahar, G De Fazio, R Franzosi, N Cristianini; Natural Language Engineering, 1-32, 2013
- Quantitative Narrative Analysis; Roberto Franzosi; Emory University © 2010
- Introduction to Stochastic Actor-Based Models for Network Dynamics - Snijders et al.
- The International Network for Social Network Analysis (INSNA) – professional society of social network analysts, with more than 1,000 members
- Center for Computational Analysis of Social and Organizational Systems (CASOS) at Carnegie Mellon
- NetLab at the University of Toronto, studies the intersection of social, communication, information and computing networks
- Netwiki (wiki page devoted to social networks; maintained at University of North Carolina at Chapel Hill)
- Program on Networked Governance – Program on Networked Governance, Harvard University
- The International Workshop on Social Network Analysis and Mining (SNA-KDD) - An annual workshop on social network analysis and mining, with participants from computer science, social science, and related disciplines.
- Historical Dynamics in a time of Crisis: Late Byzantium, 1204–1453 (a discussion of social network analysis from the point of view of historical studies)
- Social Network Analysis: A Systematic Approach for Investigating
- Social Networks
- Network Science
- Journal of Social Structure
- Journal of Complex Networks
- Journal of Mathematical Sociology
- Social Network Analysis and Mining (SNAM)
- "Connections". Toronto: International Network for Social Network Analysis. ISSN 0226-1766.
Textbooks and educational resources
- Networks, Crowds, and Markets (2010) by D. Easley & J. Kleinberg
- Introduction to Social Networks Methods (2005) by R. Hanneman & M. Riddle
- Analyzing the Social Web (2013) by J. Golbeck
|Wikimedia Commons has media related to Social networks.|
- Pajek's list of lists of datasets
- UC Irvine Network Data Repository
- Stanford Large Network Dataset Collection
- M.E.J. Newman datasets
- Pajek datasets
- Gephi datasets
- KONECT - Koblenz network collection
- RSiena datasets