Information about Social Network
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 interdependency, 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.
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). 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:
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 collude in setting prices or policies.
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 minimized, and the modern society – "organic solidarity" – that develops out of cooperation between differentiated individuals with independent roles.
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, J.L. Moreno pioneered the systematic recording and analysis of social interaction in small groups, especially classrooms and work groups (sociometry), while a Harvard group led by W. Lloyd Warner and Elton Mayo explored interpersonal relations at work. In 1940, A.R. Radcliffe-Brown's presidential address to British anthropologists urged the systematic study of networks.[2] However, it took about 15 years before this call was followed-up systematically.
Social network analysis developed with the kinship studies of Elizabeth Bott in England in the 1950s and the 1950s-1960s urbanization studies of the University of Manchester group of anthropologists (centered around Max Gluckman and later J. Clyde Mitchell) investigating community networks in southern Africa, India and the United Kingdom. Concomittantly, British anthropologist S.F. Nadel codified a theory of social structure that was influential in later network analysis.[3]
In the 1960s-1970s, a growing number of scholars worked to combine the different tracks and traditions. One large group was centered around Harrison White and his students at Harvard University: Ivan Chase, Bonnie Erickson, Harriet Friedmann, Mark Granovetter, Nancy Howell, Joel Levine, Nicholas Mullins, John Padgett, Michael Schwartz and Barry Wellman. White's group thought of themselves as rebelling against the reigning structural-functionalist orthodoxy of then-dominant Harvard sociologist Talcott Parsons, leading them to devalue concerns with sybols, values, norms and culture. They also were opposed to the methodological individualism espoused by another Harvard sociologist, George Homans, which was endemic among the dominant survey researchers and positivists of the time. are among the former students of White who have developed social network analysis. [4]
White's was not the only group. Significant independent work was done by scholars elsewhere: University of California Irvine social scientists interested in mathematical applications, centered around Linton Freeman, including John Boyd, Susan Freeman, Kathryn Faust, A. Kimball Romney and Douglas White); quantitative analysts at the University of Chicago, including Joseph Galaskiewicz, Wendy Griswold, Edward Laumann, Peter Marsden, Martina Morris, and John Padgett; and communication scholars at Michigan State University, including Nan Lin and Everett Rogers. A substantively-oriented University of Toronto sociology group developed in the 1970s, centered on former students of Harrison White: S.D. Berkowitz, Harriet Friedmann, Nancy Leslie Howard, Nancy Howell, game theorist]] Anatol Rapoport. [5]
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.[6]
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[7] that points out the many problems with the original Milgram research. A recent electronic Small World experiment[8] 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.
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.[10]
There are several journals: Social Networks, Connections, and the Journal of Social Structure.
A systematic overview and comparison of a selection of software packages for social network analysis was provided by Huisman and Van Duijn (see references). A large list of software packages and libraries can be found under Computer Programs for Social Network Analysis.
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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). 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 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 minimized, and the modern society – "organic solidarity" – that develops out of cooperation between differentiated individuals with independent roles.
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, J.L. Moreno pioneered the systematic recording and analysis of social interaction in small groups, especially classrooms and work groups (sociometry), while a Harvard group led by W. Lloyd Warner and Elton Mayo explored interpersonal relations at work. In 1940, A.R. Radcliffe-Brown's presidential address to British anthropologists urged the systematic study of networks.[2] However, it took about 15 years before this call was followed-up systematically.
Social network analysis developed with the kinship studies of Elizabeth Bott in England in the 1950s and the 1950s-1960s urbanization studies of the University of Manchester group of anthropologists (centered around Max Gluckman and later J. Clyde Mitchell) investigating community networks in southern Africa, India and the United Kingdom. Concomittantly, British anthropologist S.F. Nadel codified a theory of social structure that was influential in later network analysis.[3]
In the 1960s-1970s, a growing number of scholars worked to combine the different tracks and traditions. One large group was centered around Harrison White and his students at Harvard University: Ivan Chase, Bonnie Erickson, Harriet Friedmann, Mark Granovetter, Nancy Howell, Joel Levine, Nicholas Mullins, John Padgett, Michael Schwartz and Barry Wellman. White's group thought of themselves as rebelling against the reigning structural-functionalist orthodoxy of then-dominant Harvard sociologist Talcott Parsons, leading them to devalue concerns with sybols, values, norms and culture. They also were opposed to the methodological individualism espoused by another Harvard sociologist, George Homans, which was endemic among the dominant survey researchers and positivists of the time. are among the former students of White who have developed social network analysis. [4]
White's was not the only group. Significant independent work was done by scholars elsewhere: University of California Irvine social scientists interested in mathematical applications, centered around Linton Freeman, including John Boyd, Susan Freeman, Kathryn Faust, A. Kimball Romney and Douglas White); quantitative analysts at the University of Chicago, including Joseph Galaskiewicz, Wendy Griswold, Edward Laumann, Peter Marsden, Martina Morris, and John Padgett; and communication scholars at Michigan State University, including Nan Lin and Everett Rogers. A substantively-oriented University of Toronto sociology group developed in the 1970s, centered on former students of Harrison White: S.D. Berkowitz, Harriet Friedmann, Nancy Leslie Howard, Nancy Howell, game theorist]] Anatol Rapoport. [5]
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.[6]
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[7] that points out the many problems with the original Milgram research. A recent electronic Small World experiment[8] 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.[9]
- 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 until recently was 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.[10]
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 [1], Pajek [2], ORA [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.A systematic overview and comparison of a selection of software packages for social network analysis was provided by Huisman and Van Duijn (see references). A large list of software packages and libraries can be found under Computer Programs for Social Network Analysis.
See also
- Actor Network Theory
- Augmented Social Network (Links people, organizations and concepts)
- Community of Practice
- Dynamic Network Analysis
- 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 Potential
- Social networking service
- Social safety net
- Social Web
- Socio-technical systems
- Value network
- Virtual community
- Virtual organization
References
- 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
- Huisman, M. and Van Duijn, M. A. J. (2005). Software for Social Network Analysis. In P J. Carrington, J. Scott, & S. Wasserman (Editors), Models and Methods in Social Network Analysis (pp. 270-316). New York: Cambridge University Press.
- 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.
- Manski, Charles F. (2000). "Economic Analysis of Social Interactions". Journal of Economic Perspectives 14: 115-36. 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. http://www2.asanet.org/journals/ASRFeb03MoodyWhite.pdf
- Newman, Mark (2003). "The Structure and Function of Complex Networks". SIAM Review 56: 167-256. pdf
- 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. ^ A.R. Radcliffe-Brown, "On Social Structure," Journal of the Royal Anthropological Institute: 70 (1940): 1-12.
3. ^ [Nadel, SF. 1957. The Theory of Social Structure. London: Cohen and West.
4. ^ Mark Granovetter, "Introduction for the French Reader," Sociologica 2 (2007): 1-8; Wellman, Barry. 1988. "Structural Analysis: From Method and Metaphor to Theory and Substance." Pp. 19-61 in Social Structures: A Network Approach, edited by Barry Wellman and S.D. Berkowitz. Cambridge: Cambridge University Press.
5. ^ Mark Granovetter, "Introduction for the French Reader," Sociologica 2 (2007): 1-8; Wellman, Barry. 1988. "Structural Analysis: From Method and Metaphor to Theory and Substance." Pp. 19-61 in Social Structures: A Network Approach, edited by Barry Wellman and S.D. Berkowitz. Cambridge: Cambridge University Press. (see also Scott, 2000 and Freeman, 2004).
6. ^ 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.
7. ^ Could It Be A Big World After All?: Judith Kleinfeld article.
8. ^ Electronic Small World Experiment: Columbia.edu website.
9. ^ Moody, James, and Douglas R. White (2003). "Structural Cohesion and Embeddedness: A Hierarchical Concept of Social Groups." American Sociological Review 68(1):103-127. [8]
10. ^ : accessed through The University of North Carolina website.
2. ^ A.R. Radcliffe-Brown, "On Social Structure," Journal of the Royal Anthropological Institute: 70 (1940): 1-12.
3. ^ [Nadel, SF. 1957. The Theory of Social Structure. London: Cohen and West.
4. ^ Mark Granovetter, "Introduction for the French Reader," Sociologica 2 (2007): 1-8; Wellman, Barry. 1988. "Structural Analysis: From Method and Metaphor to Theory and Substance." Pp. 19-61 in Social Structures: A Network Approach, edited by Barry Wellman and S.D. Berkowitz. Cambridge: Cambridge University Press.
5. ^ Mark Granovetter, "Introduction for the French Reader," Sociologica 2 (2007): 1-8; Wellman, Barry. 1988. "Structural Analysis: From Method and Metaphor to Theory and Substance." Pp. 19-61 in Social Structures: A Network Approach, edited by Barry Wellman and S.D. Berkowitz. Cambridge: Cambridge University Press. (see also Scott, 2000 and Freeman, 2004).
6. ^ 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.
7. ^ Could It Be A Big World After All?: Judith Kleinfeld article.
8. ^ Electronic Small World Experiment: Columbia.edu website.
9. ^ Moody, James, and Douglas R. White (2003). "Structural Cohesion and Embeddedness: A Hierarchical Concept of Social Groups." American Sociological Review 68(1):103-127. [8]
10. ^ : accessed through The University of North Carolina website.
External links
- The International Network for Social Network Analysis (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 complicated and complex networks
- Dynamic Centrality in Social Networks
- 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
- Social Life of Routers - social network analysis applied to computer systems
- FAS.research - network visualizations produced using social network analysis
A social network service focuses on the building and verifying of online social networks for communities of people who share interests and activities, or who are interested in exploring the interests and activities of others, and which necessitates the use of software.
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A virtual community, e-community or online community is a group of people that primarily interact via communication media such as letters, telephone, email or Usenet rather than face to face.
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Social structure is a term frequently used in sociology and more specifically in social theory — yet rarely defined or clearly conceptualised (Jary and Jary 1991, Abercrombie et al 2000).
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Value is a concept that describes the beliefs of an individual or culture. A set of values may be placed into the notion of a value system. Values are considered subjective and vary across people and cultures.
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Vision may mean:
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- Visual perception via the visual system; one of the senses
- Computer vision, a field of artificial intelligence
- Vision (religion), inspirational renderings believed to come from a deity
- A visual or generic hallucination
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IDEA may refer to:
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- Electronic Directory of the European Institutions
- IDEA Center
- IDEA League
- Ieros Desmos Ellinon Axiomatikon
- Improvement and Development Agency
- Individuals with Disabilities Education Act
- Indian Distance Education Association
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FINANCIAL is the weekly English-language newspaper with offices in Tbilisi, Georgia and Kiev, Ukraine. Published by Intelligence Group LLC, FINANCIAL is focused on opinion leaders and top business decision-makers; It's about world’s largest companies, investing, careers, and
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Friendship is a term used to denote co-operative and supportive behavior between two or more humans. This article focuses on the notion specific to interpersonal relationships.
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Kinship is one of the most basic principles for organizing individuals into social groups, roles, and categories. It was originally thought to reflect biological descent, a view that was challenged by David M.
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Conflict is a state of discord caused by the actual or perceived opposition of needs, values and interests between people. The result is stress or tension and negative feelings between disputants.
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Trade is the voluntary exchange of goods, services, or both. Trade is also called commerce. A mechanism that allows trade is called a market. The original form of trade was barter, the direct exchange of goods and services.
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WEB is a computer programming system created by Donald Knuth as the first implementation of what he called "literate programming": the idea that one could create software as works of literature, by embedding source code inside descriptive text, rather than the reverse (as is common
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- This article is about sexual practices (i.e., physical sex). Broader aspects of sexual behaviour such as social and psychological sexual issues are covered in related articles such as human sexuality, heterosexuality, and homosexuality.
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Disease transmission is the way that an infectious disease or pathogen is "passed on" or "communicated" from one individual to another in a population of humans, or in groups of other animals.
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Epidemiology is the study of factors affecting the health and illness of populations, and serves as the foundation and logic of interventions made in the interest of public health and preventive medicine.
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airline provides air transport services for passengers or freight, generally with a recognized operating certificate or license. Airlines lease or own their aircraft with which to supply these services and may form partnerships or alliances with other airlines for mutual benefit.
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Social capital, referring to connections within and between social networks, is a core concept in business, economics, organisational behaviour, political science, public health, and sociology.
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Network theory is a subject within applied mathematics and physics, and coincides with graph theory. It has application in a varied range of disciplines including computer science, biology, economics, and sociology.
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Sociology (from Latin: socitus, "companion"; and the suffix -ology, "the study of", from Greek λόγος, lógos, "knowledge") is the systematic and scientific study of society and societal behavior.
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Anthropology (from Greek: ἄνθρωπος, anthropos, "human being"; and λόγος, logos, "speech" lit. to talk about human beings) is the study of humanity.
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Sociolinguistics is the study of the effect of any and all aspects of society, including cultural norms, expectations, and context on the way language is used. Sociolinguistics overlaps to a considerable degree with pragmatics.
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Geography - (from the Greek words Geo (γη) or Gaea (γαία), both meaning "Earth", and graphein (γράφειν) meaning "to describe" or "to write"
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Social psychology is the study of how social conditions affect human beings. Scholars in this field are generally either psychologists or sociologists, though all social psychologists employ both the individual and the group as their units of analysis.
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The European tradition of communication studies partly builds on the work of the Frankfurt School. The American tradition is better known for, but not limited to, Communication Sciences.
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Information science (also information studies) is an interdisciplinary science primarily concerned with the collection, classification, manipulation, storage, retrieval and dissemination of information.
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Organizational studies, organizational behaviour, and organizational theory are related terms for the academic study of organizations, examining them using the methods of economics, sociology, political science, anthropology, communication studies, and psychology.
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Economics is the social science that studies the production, distribution, and consumption of goods and services. The term economics comes from the Greek for oikos (house) and nomos (custom or law), hence "rules of the house(hold).
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Biology (from Greek: βίος, bio, "life"; and λόγος, logos, "knowledge"), also referred to as the biological sciences, is the scientific study of life.
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Group can refer to:
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- Group (auto racing)
- Group identifier (Unix)
- Business group, a collection of legally distinct business entities
- Group (travel), a set of individuals traveling together
- A musical ensemble or band
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