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Social network analysis

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Not to be confused with social network services such as Friendster, etc..

A social network is a social structure made of nodes (which are generally individuals or organizations) that are tied by one or more specific types of relations, such as values, visions, idea, financial exchange, friends, kinship, dislike, conflict, trade, web links, sexual relations, disease transmission (epidemiology), or airline routes.

Social network analysis views social relationships in terms of nodes and ties. Nodes are the individual actors within the networks, and ties are the relationships between the actors. There can be many kinds of ties between the nodes. Research in a number of academic fields has shown that social networks operate on many levels, from families up to the level of nations, and play a critical role in determining the way problems are solved, organizations are run, and the degree to which individuals succeed in achieving their goals.

In its simplest form, a social network is a map of all of the relevant ties between the nodes being studied. The network can also be used to determine the social capital of individual actors. These concepts are often displayed in a social network diagram, where nodes are the points and ties are the lines.

An example of a social network diagram

Social network analysis

Social network analysis (related to network theory) has emerged as a key technique in modern sociology, anthropology, sociolinguistics, geography, social psychology, communication studies, information science, organizational studies, economics, and biology as well as a popular topic of speculation and study.

People have used the social network metaphor for over a century to connote complex sets of relationships between members of social systems at all scales, from interpersonal to international. Yet not until J. A. Barnes in 1954 did social scientists start using the term systematically to denote patterns of ties that cut across the concepts traditionally used by the public and social scientists: bounded groups (e.g., tribes, families) and social categories (e.g., gender, ethnicity). Social network analysis developed with the kinship studies of Elizabeth Bott in England in the 1950s and the urbanization studies "Manchester School" (centered around Max Gluckman and later J. Clyde Mitchell), done mainly in Zambia during the 1960s. It joined with the field of sociometry, begun by Jacob L. Moreno in the 1930s as an attempt to quantify social relationships. Scholars such as S.D. Berkowitz, Stephen Borgatti, Ronald Burt, Linton Freeman, Mark Granovetter, Nicholas Mullins, Anatol Rapoport, Stanley Wasserman, Barry Wellman and Harrison White expanded the use of social networks.

Social network analysis has now moved from being a suggestive metaphor to an analytic approach to a paradigm, with its own theoretical statements, methods and research tribes. Analysts reason from whole to part; from structure to relation to individual; from behavior to attitude. They either study whole networks, all of the ties containing specified relations in a defined population, or personal networks, the ties that specified people have, such as their "personal communities".

Several analytic tendencies distinguish social network analysis:

There is no assumption that groups are the building blocks of society: the approach is open to studying less-bounded social systems, from nonlocal communities to links among Web sites.
Rather than treating individuals (persons, organizations, states) as discrete units of analysis, it focuses on how the structure of ties affects individuals and their relationships.
By contrast with analyses that assume that socialization into norms determines behavior, network analysis looks to see the extent to which the structure and composition of ties affect norms.

The shape of a social network helps determine a network's usefulness to its individuals. Smaller, tighter networks can be less useful to their members than networks with lots of loose connections (weak ties) to individuals outside the main network. More open networks, with many weak ties and social connections, are more likely to introduce new ideas and opportunities to their members than closed networks with many redundant ties. In other words, a group of friends who only do things with each other already share the same knowledge and opportunities. A group of individuals with connections to other social worlds is likely to have access to a wider range of information. It is better for individual success to have connections to a variety of networks rather than many connections within a single network. Similarly, individuals can exercise influence or act as brokers within their social networks by bridging two networks that are not directly linked (called filling structural holes).

The power of social network analysis stems from its difference from traditional social scientific studies, which assume that it is the attributes of individual actors -- whether they are friendly or unfriendly, smart or dumb, etc. -- that matter. Social network analysis produces an alternate view, where the attributes of individuals are less important than their relationships and ties with other actors within the network. This approach has turned out to be useful for explaining many real-world phenomena, but leaves less room for individual agency, the ability for individuals to influence their success, because so much of it rests within the structure of their network.

Social networks have also been used to examine how organizations interact with each other, characterizing the many informal connections that link executives together, as well as associations and connections between individual employees at different organizations. For example, power within organizations often comes more from the degree to which an individual within a network is at the center of many relationships than actual job title. Social networks also play a key role in hiring, in business success, and in job performance. Networks provide ways for companies to gather information, deter competition, and even collude in setting prices or policies.

History of social network analysis

A summary of the progress of social networks and social network analysis has been written by Linton Freeman. His 2004 book, The Development of Social Network Analysis[1] is especially useful for developments until the 1980s.

Precursors of social networks in the late 1800s include Émile Durkheim and Ferdinand Tönnies. Tönnies argued that social groups can exist as personal and direct social ties that either link individuals who share values and belief ("gemeinschaft") or impersonal, formal and instrumental social links ("gesellschaft"). Durkheim gave a non-individualistic explanation of social facts arguing that social phenomena arise when interacting individuals constitute a reality that can no longer be accounted for in terms of the properties of individual actors. He distinguished between a traditional society – "mechanical solidarity" – which prevails if individual differences are minimalized, and the modern society – "organic solidarity" – which develops out of differences of individuals what results in cooperation of individuals.

Georg Simmel, writing at the turn of the twentieth century, was the first scholar to think directly in social network terms. His essays pointed to the nature of network size on interaction and to the likelihood of interaction in ramified, loosely-knit networks rather than groups (Simmel, 1908/1971).

After a hiatus in the first decades of the twentieth century, three main traditions in social networks appeared. In the 1930s, one tradition worked on sociometric analysis of small groups, J.L. Moreno taking the lead in studying classrooms and work groups. A Harvard group with W. Lloyd Warner and Elton Mayo explored interpersonal relations at work. In the 1950s-1960s, anthropologists centered around the University of Manchester, such as A.R. Radcliffe-Brown, Max Gluckmann, J. Clyde Mitchell and Elizabeth Bott investigated community networks in southern Africa, India and the United Kingdom.

In the 1960’s, Harrison White at Harvard University was able to combine the different tracks and traditions (see Scott, 2000 and Freeman, 2004 for elaborate explanations of all three traditions and White’s work). Mark Granovetter and Barry Wellman are former students of White who have elaborated and popularized social networks.

Applications

The evolution of social networks can sometimes be modeled by the use of agent based models, providing insight into the interplay between communication rules, rumor spreading and social structure. Here is an interactive model of rumour spreading, based on rumour spreading from model on Cmol.

Diffusion of innovations theory explores social networks and their role in influencing the spread of new ideas and practices. Change agents and opinion leaders often play major roles in spurring the adoption of innovations, although factors inherent to the innovations also play a role.

Dunbar's number: The so-called rule of 150, asserts that the size of a genuine social network is limited to about 150 members. The rule arises from cross-cultural studies in sociology and especially anthropology of the maximum size of a village (in modern parlance most reasonably understood as an ecovillage). It is theorized in evolutionary psychology that the number may be some kind of limit of average human ability to recognize members and track emotional facts about all members of a group. However, it may be due to economics and the need to track "free riders", as it may be easier in larger groups to take advantage of the benefits of living in a community without contributing to those benefits.

Guanxi is a central concept in Chinese society that can be summarized as the use of personal influence. Guanxi can be studied from a social network approach.[2]

The small world phenomenon is the hypothesis that the chain of social acquaintances required to connect one arbitrary person to another arbitrary person anywhere in the world is generally short. The concept gave rise to the famous phrase six degrees of separation after a 1967 small world experiment by psychologist Stanley Milgram. In Milgram's experiment, a sample of US individuals were asked to reach a particular target person by passing a message along a chain of acquaintances. The average length of successful chains turned out to be about five intermediaries or six separation steps (the majority of chains in that study actually failed to complete). Academic researchers continue to explore this phenomenon. Judith Kleinfeld has written an article[3] that points out the many problems with the original Milgram research. A recent electronic Small World experiment[4] at Columbia University showed that about five to seven degrees of separation are sufficient for connecting any two people through e-mail.

The study of socio-technical systems is loosely linked to social network analysis, and looks at relations among individuals, institutions, objects and technologies.

Metrics (Measures) in social network analysis

Betweenness
Degree an individual lies between other individuals in the network; the extent to which a node is directly connected only to those other nodes that are not directly connected to each other; an intermediary; liaisons; bridges. Therefore, it's the number of people who a person is connected to indirectly through their direct links.
Closeness
The degree an individual is near all other individuals in a network (directly or indirectly). It reflects the ability to access information through the "grapevine" of network members. Thus, closeness is the inverse of the sum of the shortest distances between each individual and every other person in the network.
Centrality Degree
The count of the number of ties to other actors in the network. See also degree (graph theory).
Flow betweenness Centrality
The degree that a node contributes to sum of maximum flow between all pairs of nodes (not that node).
Centrality Eigenvector
Eigenvector centrality is a measure of the importance of a node in a network. It assigns relative scores to all nodes in the network based on the principle that connections to nodes having a high score contribute more to the score of the node in question.
Centralization
The difference between the n of links for each node divided by maximum possible sum of differences. A centralized network will have much of its links dispersed around one or a few nodes, while a decentralized network is one in which there is little variation between the n of links each node possesses
Clustering Coefficient
The clustering coefficient is a measure of the likelihood that two associates of a node are associates themselves. A higher clustering coefficient indicates a greater 'cliquishness'.
Cohesion
Refers to the degree to which actors are connected directly to each other by cohesive bonds. Groups are identified as ‘cliques’ if every actor is directly tied to every other actor, ‘social circles’ if there is less stringency of direct contact, which is imprecise, or as structurally cohesive blocks if precision is wanted.
Density
Individual-level density is the degree a respondent's ties know one another/ proportion of ties among an individual's nominees. Network or global-level density is the proportion of ties in a network relative to the total number possible (sparse versus dense networks).
Path Length
The distances between pairs of nodes in the network. Average path-length is the average of these distances between all pairs of nodes.
Radiality
Degree an individual’s network reaches out into the network and provides novel information and influence
Reach
The degree any member of a network can reach other members of the network.
Structural Cohesion
The minimum number of members who, if removed from a group, would disconnect the group.[5]
Structural Equivalence
Refers to the extent to which actors have a common set of linkages to other actors in the system. The actors don’t need to have any ties to each other to be structurally equivalent.
Structural Hole
Static holes that can be strategically filled by connecting one or more links to link together other points. Linked to ideas of social capital: if you link to two people who are not linked you can control their communication.

Professional association and journals

The International Network for Social Network Analysis is the professional association of social network analysis. Started in 1977 by Barry Wellman at the University of Toronto, it now has more than 1200 members and is headed by William Richards (Simon Fraser University).

Netwiki is a scientific wiki devoted to network theory, which uses tools from subjects such as graph theory, statistical mechanics, and dynamical systems to study real-world networks in the social sciences, technology, biology, etc.[6]

There are several journals: Social Networks, Connections, and the Journal of Social Structure.

Network analytic software

Many social network tools for scholarly work are available online such as the long time standard UCINet [2], Pajek [3], or the "network" package in "R"). They are relatively easy to use to present graphical images of networks. Business oriented software is also available. Examples include InFlow[4], NetMiner [5]. An open source package for linux is Social Networks Visualizer or SocNetV [6]; a related package installer of SocNetV for Mac OS X [7] is available.

See also

References

Print
  • Barnes, J. A. "Class and Committees in a Norwegian Island Parish", Human Relations 7:39-58
  • Berkowitz, S. D. 1982. An Introduction to Structural Analysis: The Network Approach to Social Research. Toronto: Butterworth.
  • Brandes, Ulrik, and Thomas Erlebach (Eds.). 2005. Network Analysis: Methodological Foundations Berlin, Heidelberg: Springer-Verlag.
  • Breiger, Ronald L. 2004. "The Analysis of Social Networks." Pp. 505-526 in Handbook of Data Analysis, edited by Melissa Hardy and Alan Bryman. London: Sage Publications. Excerpts in pdf format
  • Burt, Ronald S. (1992). Structural Holes: The Structure of Competition. Cambridge, MA: Harvard University Press.
  • Carrington, Peter J., John Scott and Stanley Wasserman (Eds.). 2005. Models and Methods in Social Network Analysis. New York: Cambridge University Press.
  • Christakis, Nicholas and James H. Fowler "The Spread of Obesity in a Large Social Network Over 32 Years," New England Journal of Medicine 357 (4): 370-379 (26 July 2007)
  • Doreian, Patrick, Vladimir Batagelj, and Anuska Ferligoj. (2005). Generalized Blockmodeling. Cambridge: Cambridge University Press.
  • Freeman, Linton C. (2004) The Development of Social Network Analysis: A Study in the Sociology of Science. Vancouver: Empirical Press.
  • Hill, R. and Dunbar, R. 2002. "Social Network Size in Humans." Human Nature, Vol. 14, No. 1, pp. 53-72.Google
  • Jackson, Matthew O. (2003). "A Strategic Model of Social and Economic Networks". Journal of Economic Theory. 71: 44–74. pdf
  • Krebs, Valdis (2006) Social Network Analysis, A Brief Introduction. (Includes a list of recent SNA applications Web Reference.)
  • Lin, Nan, Ronald S. Burt and Karen Cook, eds. (2001). Social Capital: Theory and Research. New York: Aldine de Gruyter.
  • Mullins, Nicholas. 1973. Theories and Theory Groups in Contemporary American Sociology. New York: Harper and Row.
  • Müller-Prothmann, Tobias (2006): Leveraging Knowledge Communication for Innovation. Framework, Methods and Applications of Social Network Analysis in Research and Development, Frankfurt a. M. et al.: Peter Lang, ISBN 0-8204-9889-0.
  • Newman, Mark, 2003. "The Structure and Function of Complex Networks." SIAM Review 45: 167-256. [http://www.santafe.edu/files/gems/paleofoodwebs/Newman2003SIAM.pdf pdf
  • Manski, Charles F. (2000). "Economic Analysis of Social Interactions". Journal of Economic Perspectives. 14: 115–36. [8] via JSTOR
  • Moody, James, and Douglas R. White (2003). "Structural Cohesion and Embeddedness: A Hierarchical Concept of Social Groups." American Sociological Review 68(1):103-127. [9]
  • Nohria, Nitin and Robert Eccles (1992). Networks in Organizations. second ed. Boston: Harvard Business Press.
  • Nooy, Wouter d., A. Mrvar and Vladimir Batagelj. (2005). Exploratory Social Network Analysis with Pajek. Cambridge: Cambridge University Press.
  • Scott, John. (2000). Social Network Analysis: A Handbook. 2nd Ed. Newberry Park, CA: Sage.
  • Tilly, Charles. (2005). Identities, Boundaries, and Social Ties. Boulder, CO: Paradigm press.
  • Valente, Thomas. (1995). Network Models of the Diffusion of Innovation. Cresskill, NJ: Hampton Press.
  • Wasserman, Stanley, & Faust, Katherine. (1994). Social Networks Analysis: Methods and Applications. Cambridge: Cambridge University Press.
  • Watkins, Susan Cott. (2003). "Social Networks." Pp. 909-910 in Encyclopedia of Population. rev. ed. Edited by Paul Demeny and Geoffrey McNicoll. New York: Macmillan Reference.
  • Watts, Duncan. (2003). Small Worlds: The Dynamics of Networks between Order and Randomness. Princeton: Princeton University Press.
  • Watts, Duncan. (2004). Six Degrees: The Science of a Connected Age. W. W. Norton & Company.
  • Wellman, Barry (1999). Networks in the Global Village. Boulder, CO: Westview Press.
  • Wellman, Barry. 2001. "Physical Place and Cyber-Place: Changing Portals and the Rise of Networked Individualism." International Journal for Urban and Regional Research 25 (2): 227-52.
  • Wellman, Barry and Berkowitz, S.D. (1988). Social Structures: A Network Approach. Cambridge: Cambridge University Press.
  • Weng, M. (2007). A Multimedia Social-Networking Community for Mobile Devices Interactive Telecommunications Program, Tisch School of the Arts/ New York University
  • White, Harrison, Scott Boorman and Ronald Breiger. 1976. "Social Structure from Multiple Networks: I Blockmodels of Roles and Positions." American Journal of Sociology 81: 730-80.
Internet
  1. ^ Vancouver: Empirical Press
  2. ^ Barry Wellman, Wenhong Chen and Dong Weizhen. “Networking Guanxi." Pp. 221-41 in Social Connections in China: Institutions, Culture and the Changing Nature of Guanxi, edited by Thomas Gold, Douglas Guthrie and David Wank. Cambridge University Press, 2002.
  3. ^ Could It Be A Big World After All?: Judith Kleinfeld article.
  4. ^ Electronic Small World Experiment: Columbia.edu website.
  5. ^ Moody, James, and Douglas R. White (2003). "Structural Cohesion and Embeddedness: A Hierarchical Concept of Social Groups." American Sociological Review 68(1):103-127. [1]
  6. ^ Netwiki: accessed through The University of North Carolina website.
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