Social network analysis

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, 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, information science and organizational studies, 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 J.L. Moreno in the 1930s, an attempt to quantify social relationships. Scholars such as Mark Granovetter, 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 networks

A comprehensive summary of the theoretical progress of social networks and social network analysis is written by Linton Freeman. His book, The development of social network analysis, gives great insight in the development of the social theories.

The first statements of structural perspectives on social sciences is given by August Comte. His works in the mid 1700’s had great influence on the further progress of the social theory. The late 1800’s were influenced by four big thinkers and theorists, namely Max Weber, Emile Durkheim, Karl Marx and Ferninand Tonnies.

Where Tonnies and Weber focused on motivational factors for actions of individuals (methodological individualism; Freeman, 2004) by saying that social groups can exist as direct, personal and direct social ties which will link individuals who share values and beliefs, called gemeinschaft; and impersonal, formal and instrumental social links, called gesellschaft (Tonnies, see Freeman pp 14), Durkheim engaged into the research of the so-called social facts in which he explains 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” (Coser, 1977 pp3 out Durkheim, 1950). He distinguishes a traditional society – solidarite mechanique – which prevails if individual differences individual differences are minimalized, and the modern society – solidarite organique – which develops out of differences of individuals what results in cooperation of individuals.

The first sociologists who attempted to find a all-covering sociology theory and elaborated further on Tonnies work about social ties were Georg Simmel and the much lesser known Hanz Sturiengen. The first traces of the modern social networks definition can be found in his work by saying that “a society exists where a number of individuals enter into interaction” (Simmel, 1908/1971, pp23).

From this point three main traditions in social networks appeared. The previously worked on sociometric analysis with Kohler and Moreno who worked on small groups, the Harvard group with Warner and Mayo who explored interpersonal relationships and ties and the Manchester anthropologists who investigated communities.

In the 1960’s White 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). Bellman was the last of the great sociologists who elaborated and popularized social networks. He is also very involved in the online community research.

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 (sometimes called Dunbar's number). 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 in China, the use of personal influence, can be studied from a social network approach.

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 that points out the many problems with the original Milgram research. A recent electronic Small World experiment at Columbia University showed that about five to seven degrees of separation are sufficient for connecting any two people through e-mail.

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.

Centrality 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.

Contagion

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).

Integration

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.

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.

Social networking / Internet social networks

Main article: Social network service

See also: List of social networking websites, Motivations for contributing to online communities

The first social networking website was Classmates.com, which began in 1995. Other sites followed, including SixDegrees.com, which began in 1997 using the Web of Contacts model. The year 1999 saw the development of two competing models of social networking, the Circle of Trust developed by Epinions and utilised by Ciao.com, Dooyoo and ToLuna and the Circle of Friends developed by Jonathan Bishop, which was utilised on a number of regional UK sites between 1999 and 2001 and flourished with the advent of a website called Friendster in 2002. This is now one of the most dominant methods of social networking in virtual communities, perhaps for the reason that it gives the user control rather than being computer controlled. There were over 50 social networking sites using the Circle of Friends in 2005 when one such online community, MySpace, was getting more page views than Google. Google has a social network called Orkut, launched in 2004. Social networking began to be seen as a component of internet strategy at around the same time: in March 2005 Yahoo launched Yahoo! 360°, their entry into the field, and in July 2005 News Corporation bought Circle of Friends-based MySpace, followed by ITV buying Old Boy Network-based Friends Reunited in December that year. It is estimated that combined there are now over 200 social networking sites using these existing and emerging social networking models.

In these communities, an initial set of founders sends out messages inviting members of their own personal networks to join the site. New members repeat the process, growing the total number of members and links in the network. Sites then offer features such as automatic address book updates, viewable profiles, the ability to form new links through "introduction services," and other forms of online social connections. Social networks can also be organized around business connections, as in the case of LinkedIn.

Blended networking is an approach to social networking that combines both offline elements (face-to-face events) and online elements. MySpace, for example, builds on independent music and party scenes, and Facebook was originally designed to mirror a college community, though it has since expanded its scope to include high school, job-related, and regional networks. The newest social networks on the Internet are becoming more focused on niches such as travel, art, tennis, football (soccer), golf, cars, dog owners, and even cosmetic surgery. Other social networking sites focus on local communities, sharing local business and entertainment reviews, news, event calendars and happenings. See also Social computing.

Most of the social networks on the internet are public, allowing anyone to join. Organizations, such as large companies, also have access to private social networking applications, known as Enterprise Relationship Management. Microsoft released an enterprise social networking application in the form of a free add-on for Microsoft Office SharePoint Server called Knowledge Network (currently in beta) in February 2007. Organizations install these applications on their own servers and enable employees to share their networks of contacts and relationships to outside people and companies.

There are many discussions as to where social networking is headed next. The advent of the Internet has enabled informal social networks to connect with people globally and with time shifting (through email), although in practice, most interactions are with people who live and work nearby.

A new type of social network are links between web pages. These can be studied in their own right (i.e., where are the hubs?) and as links between individual's web pages in social software where individuals begin with their address book, and expand their network by adding friends, "friendster" acquaintances and imaginary friends. This creates connectivity through being discovered through friends of friends, etc. Future applications may allow for discovering the social networks of others by stumbling upon them.

The growth in community adoption is often forecasted (that is, estimating the number of users in the community) by use of the Bass diffusion model, a mathematical formula originally conceived by Frank Bass to describe the process how new products get adopted as an interaction between users and potential users.

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.

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. A business orientated software is also available, called InFlow[4]. An open source package for linux is Social Networks Visualizer or SocNetV [5]; a related package installer of SocNetV for Mac OS X [6] is available.

See also

• Actor Network Theory
• Augmented Social Network (Links people, organizations and concepts)
• Community of Practice
• Economic network
• FOAF (Friend of a Friend)
• Guanxi
• International Network for Social Network Analysis
• Knowledge management
• Mathematical Sociology
• Mobile social network
• MoSoSo (Mobile Social Software)
• Motivations for Contributing to Online Communities
• Network analysis
• Network of practice
• Sexual network
• Six degrees of separation
• Small world phenomenon
• Social-circles network model
• Social contract
• Social networking service
• Social Networking Software (SNS)
• Social safety net
• Social Web
• Socio-technical systems
• Value network
• Virtual community
• Virtual organization

References

Print

• Barnes, J. A. "Class and Committees in a Norwegian Island Parish", Human Relations 7:39-58
• 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.
• 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
• 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.
• Newman, Mark (2003). "The Structure and Function of Complex Networks". SIAM Review. 45: 167–256. pdf
• Moody, James, and Douglas R. White (2003). "Structural Cohesion and Embeddedness: A Hierarchical Concept of Social Groups." American Sociological Review 68(1):103-127. [7]
• 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 and Berkowitz, S.D. (1988). Social Structures: A Network Approach. Cambridge: Cambridge University Press.

Internet

External links

• The International Network for Social Network Analyis (INSNA) - professional society of social network analysts, with more than 1,000 members
• Organizational Network Mapping - SNA applied in business organizations
• Virtual Center for Supernetworks
• VisualComplexity.com - a visual exploration on mapping complex networks
• Dynamic Centrality in Social Networks
• Center for Computational Analysis of Social and Organizational Systems (CASOS) at Carnegie Mellon