Social Network Analysis: Introduction and Resources


What is Social Network Analysis?

Network Data Collection and Representation

Network Theories

Analysis of Network Data

Software Applications

Books and Journals

Article References      

Selected Online SNA Portals



Ulrike Gretzel                                                                           November, 2001




What is Social Network Analysis?

Social network analysis is based on an assumption of the importance of relationships among interacting units. The social network perspective encompasses theories, models, and applications that are expressed in terms of relational concepts or processes. Along with growing interest and increased use of network analysis has come a consensus about the central principles underlying the network perspective. In addition to the use of relational concepts, we note the following as being important:

The unit of analysis in network analysis is not the individual, but an entity consisting of a collection of individuals and the linkages among them. Network methods focus on dyads (two actors and their ties), triads (three actors and their ties), or larger systems (subgroups of individuals, or entire networks.

Wasserman, S. and K. Faust, 1994, Social Network Analysis. Cambridge: Cambridge University Press.

Social network analysis has emerged as a set of methods for the analysis of social structures, methods which are specifically geared towards an investigation of the relational aspects of these structures. The use of these methods, therefore, depends on the availability of relational rather than attribute data.

Scott, J., 1992, Social Network Analysis. Newbury Park CA: Sage.

Network analysis is the study of social relations among a set of actors. It is a field of study -- a set of phenomena or data which we seek to understand. In the process of working in this field, network researchers have developed a set of distinctive theoretical perspectives as well. Some of the hallmarks of these perspectives are:

Network theory is sympathetic with systems theory and complexity theory. Social networks is also characterized by a distinctive methodology encompassing techniques for collecting data, statistical analysis, visual representation, etc.

Steve Borgatti:

Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers or other information/knowledge processing entities. The nodes in the network are the people and groups while the links show relationships or flows between the nodes. SNA provides both a visual and a mathematical analysis of complex human systems.

Network analysis (or social network analysis) is a set of mathematical methods used in social psychology, sociology, ethology, and anthropology. Network analysis assumes that the way the members of a group can communicate to each other affect some important features of that group (efficiency when performing a task, moral satisfaction, leadership). Network analysis makes use of mathematical tools and concepts that belong to graph theory. A network models a communication group. It consists of a number of nodes (each node corresponding to a member of the group) and a number of edges (or ties)¸each one being associated to a communication connection between two actors. Network data is stored in an adjacency matrix. Commonly, the [i,j] element of the adjacency matrix corresponds to the communication behavior of actor ╬i' to actor ╬j'.

Social network analysis is focused on uncovering the patterning of people's interaction. Network analysis is based on the intuitive notion that these patterns are important features of the lives of the individuals who display them. Network analysts believe that how an individual lives depends in large part on how that individual is tied into the larger web of social connections. Many believe, moreover, that the success or failure of societies and organizations often depends on the patterning of their internal structure. From the outset, the network approach to the study of behavior has involved two commitments: (1) it is guided by formal theory organized in mathematical terms, and (2) it is grounded in the systematic analysis of empirical data. It was not until the 1970s, therefore--when modern discrete combinatorics (particularly graph theory) experienced rapid development and relatively powerful computers became readily available--that the study of social networks really began to take off as an interdisciplinary specialty. Since then its growth has been rapid. It has found important applications in organizational behavior, inter-organizational relations, the spread of contagious diseases, mental health, social support, the diffusion of information and animal social organization.

Lin Freeman:


Network Data Collection and Representation



  1. Questionnaires

  2. Direct Observation

  3. Written Records: archival or diary

  4. Experiments

  5. Derivation


Types of social relations that can be represented through network data:


Kinship: brother of, father of

Social Roles: boss of, teacher of, friend of

Affective: likes, respects, hates

Cognitive: knows, views as similar

Actions: talks to, has lunch with, attacks

Flows: number of cars moving between point A and B

Transfer of material resources: business transactions, lending, etc.

Distance: number of miles between

Co-occurrence: is in the same club as, has the same hair color as

( )




Actor/Node/Point/Agent: social entities such as persons, organizations, cities, etc.

Tie/Link/Edge/Line/Arc: represents relationships among actors.

Dyad: consists of a pair of actors and the (possible) tie(s) between them.

Triad: a subset of three actors and the (possible) tie(s) among them.

Subgroup: subset of actors and all ties among them.

Group: collection of all actors on which ties are to be measured.

Relation: collection of ties of a specific kind among members of a group.

Social Network: finite set or sets of actors and the relation or relations defined on them.


Wasserman, S. and K. Faust, 1994, Social Network Analysis. Cambridge: Cambridge University Press.


Social networks can be represented as GRAPHS or MATRICES.





Network Theories

Monge, P. R., & Contractor, N. S. (in press). Emergence of communication networks. In L. Putnam & F. Jablin (Eds.) New handbook of organizational communication. Newbury Park, CA: Sage.



Theoretical Mechanisms

Theories of self-interest 

Theory of Social capital 

Strength of Weak Ties Theory 

Transaction Cost Economics


Investments in opportunities 

Control of information flow 

Minimize the cost of transactions 


Theories of mutual self-interest and collective action 

Public Goods Theory 

Critical Mass Theory


Inducements to contribute 

Number of people with resources and interests

Exchange and Dependency theories 

Social Exchange Theory 

Resource Dependency Theory

Exchange of valued resources (material or information)

Contagion theories 

Social Information Processing theory 

Social Cognitive Theory 

Institutional Theory 

Structural Theory of Action

Exposure or contact leading to: 

Social influence 

Imitation, modeling, learning 

Mimetic behavior 

Similar positions in structure and roles

Cognitive theories 

Semantic Networks 

Knowledge Structures 

Cognitive Social Structures 

Cognitive Consistency

Cognitive mechanisms leading to: 

Shared interpretations 

Knowledge transfer 

Similarity in perceptual structures 

Drive to restore balance

Homophily theories 

Social Comparison Theory 

Social Identity Theory


Choose similar others for comparison 

Choose categories to define one's own group identity

Theories of proximity 

Physical proximity 

Electronic proximity 



Influence of distance 

Influence of accessibility

Theories of uncertainty reduction 

Uncertainty reduction theory 


Contingency theory


Reduce uncertainty by communicating 


Reduce uncertainty in environment 

Social support theories

Providing instrumental, emotional, and material support from the network 





Analysis of Network Data




Actor level: centrality, prestige and roles such as isolates, liaisons, bridges, etc.

Dyadic level: distance and reachability, structural and other notions of equivalence, and tendencies toward reciprocity.

Triadic level: balance and transitivity

Subset level: cliques, cohesive subgroups, components

Network level: connectedness, diameter, centralization, density, prestige, etc.


Wasserman, S. and K. Faust, 1994, Social Network Analysis. Cambridge: Cambridge University Press.





Software Applications


INSNA links to network analysis software packages:




Network Analysis program. Available through:



Network visualization. Available through:



Helps collect and analyze structured qualitative and quantitative data including freelists, pilesorts, triads, paired comparisons, and ratings. ANTHROPAC's analytical tools include techniques that are unique to Anthropology, such as consensus analysis, as well as standard multivariate tools such as multiple regression, factor analysis, cluster analysis, multidimensional scaling and correspondence analysis. In addition, the program provides a wide variety of data manipulation and transformation tools, plus a full-featured matrix algebra language.



A different kind of network analysis program. FATCAT works with categorical who-to-whom matrices, in which you select a variable that describes nodes to determine the categories for rows (who) and another one to determine the categories for columns (whom).



One of the oldest network analysis programs, NEGOPY finds cliques, liaisons, and isolates in networks having up to 1,000 members and 20,000 links. In use at over 100 universities and research centers around the world.



StOCNET is an open software system currently under development that will provide a new platform to make a number of statistical methods that are presently privately owned available to a wider audience. A new version that contains BLOCKS and SIENA can be downloaded.



GRAph Definition and Analysis Package, can be used to define, manipulate, and analyze graphs and networks of various kinds.





Sparse matrix version of PSTAR.



Tool that assists the study, creation, and growth of knowledge networks.



Blanche is a computational modeling environment to specify, simulate, and analyze the evolution and co-evolution of networks.



Network visualization.



Package for large network analysis.



Network visualization.




Books and Journals

Scott, J., 1992, Social Network Analysis. Newbury Park CA: Sage. Online Table of Contents with excerpts:

Wasserman, S. and K. Faust, 1994, Social Network Analysis. Cambridge: Cambridge University Press.

Monge, P. R., & Contractor, N. S. (in press). Emergence of communication networks. In L. Putnam & F. Jablin (Eds.) New handbook of organizational communication. Newbury Park, CA: Sage.

Burt, R.S., and M. Minor, Applied Network Analysis: A Methodological Introduction, Newbury Park: Sage, 1983.

Freeman, L.C., D.R. White, and A.K. Romney, Research Methods in Social Network Analysis, Fairfax, VA: George Mason University Press, 1989.

Wellman, B., and S.D. Berkowitz, Social Structures: A Network Approach, Cambridge: Cambridge University Press, 1988.

Robert A. Hannemann: Introduction to Social Network Methods. Online Textbook:



Social Networks




Journal of Social Structure




Article References

Jonathon Cumming's bibliography: 

Linton Freeman's online papers:


Robert A. Hanneman's Working Bibliography on Social Network Analysis Methods:


The Vancouver Network Analysis Team's Bibliography


Excerpt from Noshir Contractor & Peter Monge's selection of readings for their Communication & Knowledge Networks Course Spring 2001, University of Illinois at Urbana-Champaign:


Stohl, C. (1995). A Network perspective. In Organizational Communication: Connectedness in Action. (pp.20-43). Newbury Park, CA: Sage.


Krackhardt, D. & Hanson, J. R. (1993). Informal Networks: The company behind the chart. Harvard Business Review, 71, 104.


Borgatti, S. P., Everett, M. G., & Freeman, L.C. (2001). UCINET V Network analysis software manual. Harvard, MA: Analytic Technologies.


Krackhardt, D., Blythe, J., & McGrath, C. (2001). KrackPlot 3.0: User's Manual. Harvard, MA: Analytic Technologies.


Broder, A., et al. (2000). Graph structure in the web.


Heald, M., Contractor, N., Koehly, L. M., & Wasserman, S. (1998). Formal and emergent predictors of coworkers' perceptual congruence on an organization's social structure. Human Communication Research, 24, 536-563.


Abrahamson, E. & Rosenkopf, L. (1997). Social network effects on the extent of innovation diffusion: A computer simulation. Organization Science, 8, 289-309.


Hyatt, A., Contractor, N. & Jones, P.M. (1997). Computational organizational network modeling: Strategies and an example. Computational and Mathematical Organizational Theory, 4, 285-300.


Contractor, N., Whitbred, R., Fonti, F., Hyatt, A., Jones, P., & O'Keefe, B. (2000). Structuration and self-organizing networks. Paper presented at the 2000 Winter Organizational Science Conference, Keystone, CO.


Anderson P. (1999). Complexity theory and organization science. Organization Science, 10(3), 216-232.


Banks, D. L. & Carley, K.M. (1996). Models for network evolution. Journal of Mathematical Sociology, 21, 173-196.


Barley, S. R. (1990). The alignment of technology and structure through roles and networks. Administrative Science Quarterly, 35, 61-103.


Zeggelink, E.P.H., Stokman, F.N., & van de Bunt, G. G. (1996). The emergence of groups in the evolution of friendship networks. Journal of Mathematical Sociology, 21, 29-55.


Burkhardt, M.E., & Brass, D.J. (1990). Changing patterns or patterns of change: The effects of a change in technology on social network structure and power. Administrative Science Quarterly, 35, 104-127.


Ahuja, G. (2000). Collaboration networks, structural holes, and innovation: A longitudinal study. Administrative Science Quarterly, 45, 425-455.


Burt, R. (October, 1998). The network structure of social capital.


Marwell, G. & Oliver, P.E. (1993). The critical mass in collective action: A micro social theory. (Social networks: density, centralization, and cliques, pp. 101-129). New York: Cambridge University Press.


Granovetter, M. (1985). Economic action and social structure: The problem of embeddedness. American Journal of Sociology, 91, 481-510.


Uzzi, B. (1997). Social structure and competition in interfirm networks: the paradox of embeddedness. Administrative Science Quarterly, 42, 35-67.


Walker, G., Kogut, B., & Shan, W. (1997) Social capital, structural holes and the formation of an industry network. Organization Science, 8, 109-125.


Monge, P., Fulk, J., Kalman, M., Flanagin, A., Parnassa, C. & Rumsey, S. (1998). Production of Collective Action in Alliance-Based Interorganizational Communication and Information Systems. Organization Science, 9, 411-433.


Oliver, P. E. (1993). Formal models of collective action. Annual Review of Sociology, 19, 271-300. 





Selected Online SNA Portals

International Network for Social Network Analysis

Links to online resources, conference information, journals, course descriptions, data resources, social network researchers and programs, software packages and listservs.


Analytic Technologies

Software download site for UCINET and Krackplot but also great and comprehensive introduction to social network analysis through its Social Network Analysis Instructional Web site that contains definitions, explanations, examples and slide shows:



Software download, user's guide, useful social network analysis links.


Vladimir Batagelj's Web page

Mainly links to various software applications.


Formation of Economic and social networks page

Annotated links to networks-related Web sites. 


Tom Snijders Social Network Analysis Page

Downloadable papers and annotated links to software download pages.


The Graphic Imaging source for Social Network Analysis

Linton Freeman's collection of links related to network visualization.


The Vancouver Network Analysis Team

Papers, bibliography, software downloads (Fatcat, Negopy, Multinet, Pspar, etc.) and links to other network researchers. 


Stanley Wasserman's pstar resources

Tutorials, workshops, literature, software download.


Noshir Contractor's homepage

Papers, projects and software.


Ronald Burt's homepage

Teaching materials and research papers.



 Last updated: November, 2001