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Social network analysis

A social network diagram displaying friendship ties among a set of Facebook users.
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Social network analysis (SNA) is the methodical analysis of social networks. Social network analysis views social relationships in terms of network theory, consisting of nodes (representing individual actors within the network) and ties (which represent relationships between the individuals, such as friendship, kinship, organizational position, sexual relationships, etc.)[1][2] These networks are often depicted in a social network diagram, where nodes are represented as points and ties are represented as lines.

Contents

Overview

Social network analysis has emerged as a key technique in modern sociology. It has also gained a significant following in anthropology, biology, communication studies, economics, geography, history, information science, organizational studies, political science, social psychology, development studies, and sociolinguistics and is now commonly available as a consumer tool.[3][4][5][6]

People have used the idea of "social network" loosely for over a century to connote complex sets of relationships between members of social systems at all scales, from interpersonal to international[citation needed]. In 1954, J. A. Barnes started using the term systematically to denote patterns of ties, encompassing concepts traditionally used by the public and those used by social scientists: bounded groups (e.g., tribes, families) and social categories (e.g., gender, ethnicity). Scholars such as S.D. Berkowitz, Stephen Borgatti, Ronald Burt, Kathleen Carley, Martin Everett, Katherine Faust, Linton Freeman, Mark Granovetter, David Knoke, David Krackhardt, Peter Marsden, Nicholas Mullins, Anatol Rapoport, Stanley Wasserman, Barry Wellman, Douglas R. White, and Harrison White expanded the use of systematic social network analysis.[7] Even in the study of literature, network analysis has been applied by Anheier, Gerhards and Romo,[8] Wouter De Nooy,[9] and Burgert Senekal.[10] Indeed, social network analysis has found applications in various academic disciplines, as well as practical applications such as countering money laundering and terrorism.

Historical development

See Social Network.

Metrics

Hue (from red=0 to blue=max) indicates each node's betweenness centrality.

Connections

Homophily: The extent to which actors form ties with similar versus dissimilar others. Similarity can be defined by gender, race, age, occupation, educational achievement, status, values or any other salient characteristic.[11]

Multiplexity: The number of content-forms contained in a tie.[12] For example, two people who are friends and also work together would have a multiplexity of 2.[13] Multiplexity has been associated with relationship strength.

Mutuality/Reciprocity: The extent to which two actors reciprocate each other’s friendship or other interaction.[14]

Network Closure: A measure of the completeness of relational triads. An individual’s assumption of network closure (i.e. that their friends are also friends) is called transitivity. Transitivity is an outcome of the individual or situational trait of Need for Cognitive Closure.[15]

Propinquity: The tendency for actors to have more ties with geographically close others.[14]

Distributions

Bridge: An individual whose weak ties fill a structural hole, providing the only link between two individuals or clusters. It also includes the shortest route when a longer one is unfeasible due to a high risk of message distortion or delivery failure.[16]

Centrality: Centrality refers to a group of metrics that aim to quantify the "importance" or "influence" (in a variety of senses) of a particular node (or group) within a network.[17][18][19][20] Examples of common methods of measuring "centrality" include betweenness centrality,[21] closeness centrality, eigenvector centrality, alpha centrality and degree centrality.[22]

Density: The proportion of direct ties in a network relative to the total number possible.[23][24]

Distance: The minimum number of ties required to connect two particular actors. Popularized by Stanley Milgram’s small world experiment and the idea of ‘six degrees of separation’.

Structural holes: The absence of ties between two parts of a network. Finding and exploiting a structural hole can give an entrepreneur a competitive advantage. This concept was developed by sociologist Ronald Burt, and is sometimes referred to as an alternate conception of social capital.

Tie Strength: Defined by the linear combination of time, emotional intensity, intimacy and reciprocity (i.e. mutuality).[16] Strong ties are associated with homophily, propinquity and transitivity, while weak ties are associated with bridges.

Segmentation

Groups are identified as ‘cliques’ if every individual is directly tied to every other individual, ‘social circles’ if there is less stringency of direct contact, which is imprecise, or as structurally cohesive blocks if precision is wanted.[25]

Clustering coefficient: A measure of the likelihood that two associates of a node are associates. A higher clustering coefficient indicates a greater 'cliquishness'.[26]

Cohesion: The degree to which actors are connected directly to each other by cohesive bonds. Structural cohesion refers to the minimum number of members who, if removed from a group, would disconnect the group.[27][28]

Modelling and visualization of networks

Visual representation of social networks is important to understand the network data and convey the result of the analysis [2]. Many of the analytic software have modules for network visualization. Exploration of the data is done through displaying nodes and ties in various layouts, and attributing colors, size and other advanced properties to nodes. Visual representations of networks may be a powerful method for conveying complex information, but care should be taken in interpreting node and graph properties from visual displays alone, as they may misrepresent structural properties better captured through quantitative analyses.[29]

Collaboration graphs can be used to illustrate good and bad relationships between humans. A positive edge between two nodes denotes a positive relationship (friendship, alliance, dating) and a negative edge between two nodes denotes a negative relationship (hatred, anger). Signed social network graphs can be used to predict the future evolution of the graph. In signed social networks, there is the concept of "balanced" and "unbalanced" cycles. A balanced cycle is defined as a cycle where the product of all the signs are positive. Balanced graphs represent a group of people who are unlikely to change their opinions of the other people in the group. Unbalanced graphs represent a group of people who are very likely to change their opinions of the people in their group. For example, a group of 3 people (A, B, and C) where A and B have a positive relationship, B and C have a positive relationship, but C and A have a negative relationship is an unbalanced cycle. This group is very likely to morph into a balanced cycle, such as one where B only has a good relationship with A, and both A and B have a negative relationship with C. By using the concept of balances and unbalanced cycles, the evolution of signed social network graphs can be predicted.[citation needed]

Especially when using social network analysis as a tool for facilitating change, different approaches of participatory network mapping have proven useful. Here participants / interviewers provide network data by actually mapping out the network (with pen and paper or digitally) during the data collection session. One benefit of this approach is that it allows researchers to collect qualitative data and ask clarifying questions while the network data is collected.[30]

See also

References

  1. ^ Pinheiro, Carlos A.R. (2011). Social Network Analysis in Telecommunications. John Wiley & Sons. p. 4. ISBN 978-1-118-01094-5. 
  2. ^ D'Andrea, Alessia et al. (2009). "An Overview of Methods for Virtual Social Network Analysis". In Abraham, Ajith et al. Computational Social Network Analysis: Trends, Tools and Research Advances. Springer. p. 8. ISBN 978-1-84882-228-3. 
  3. ^ Facebook friends mapped by Wolfram Alpha app BBC News
  4. ^ Wolfram Alpha Launches Personal Analytics Reports For Facebook Tech Crunch
  5. ^ [1]
  6. ^ Ivaldi M., Ferreri L., Daolio F., Giacobini M., Tomassini M., Rainoldi A., We-Sport: from academy spin-off to data-base for complex network analysis; an innovative approach to a new technology. J Sports Med and Phys Fitnes Vol. 51-suppl. 1 to issue No. 3. The social network analysis was used to analyze properties of the network We-Sport.com allowing a deep interpretation and analysis of the level of aggregation phenomena in the specific context of sport and physical exercise.
  7. ^ Linton Freeman, The Development of Social Network Analysis. Vancouver: Empirical Press, 2006.
  8. ^ Anheier, H.K., J. Gerhards en F.P. Romo. 1995. Forms of capital and social structure of fields: examining Bourdieu’s social topography. American Journal of Sociology, 100:859–903
  9. ^ De Nooy, W. 2003. Fields and networks: Correspondence analysis and social network analysis in the framework of Field Theory. Poetics, 31:305–27
  10. ^ Senekal, B. A. 2012. Die Afrikaanse literêre sisteem: ʼn Eksperimentele benadering met behulp van Sosiale-netwerk-analise (SNA), LitNet Akademies 9(3)
  11. ^ McPherson, N., Smith-Lovin, L., Cook, J.M. (2001). "Birds of a feather: Homophily in social networks". Annual Review of Sociology 27. pp. 415–444. 
  12. ^ Podolny, J.M. & Baron, J.N. (1997). Resources and relationships: Social networks and mobility in the workplace. American Sociological Review, 62(5), 673-693.
  13. ^ Kilduff, M., Tsai, W. (2003). Social networks and organisations. Sage Publications. 
  14. ^ a b Kadushin, C. (2012). Understanding social networks: Theories, concepts, and findings. Oxford: Oxford University Press
  15. ^ Flynn, F.J., Reagans, R.E. & Guillory, L. (2010). Do you two know each other? Transitivity, homophily, and the need for (network) closure. Journal of Personality and Social Psychology, 99(5), 855-869.
  16. ^ a b Granovetter, M. (1973). "The strength of weak ties". American Journal of Sociology 78 (6). pp. 1360–1380. 
  17. ^ Hansen, Derek et al. (2010). Analyzing Social Media Networks with NodeXL. Morgan Kaufmann. p. 32. ISBN 978-0-12-382229-1. 
  18. ^ Liu, Bing (2011). Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer. p. 271. ISBN 978-3-642-19459-7. 
  19. ^ Hanneman, Robert A. & Riddle, Mark (2011). "Concepts and Measures for Basic Network Analysis". The Sage Handbook of Social Network Analysis. SAGE. pp. 364–367. ISBN 978-1-84787-395-8. 
  20. ^ Tsvetovat, Maksim & Kouznetsov, Alexander (2011). Social Network Analysis for Startups: Finding Connections on the Social Web. O'Reilly. p. 45. ISBN 978-1-4493-1762-1. 
  21. ^ The most comprehensive reference is: Wasserman, Stanley, & Faust, Katherine. (1994). Social Networks Analysis: Methods and Applications. Cambridge: Cambridge University Press. A short, clear basic summary is in Krebs, Valdis. (2000). "The Social Life of Routers." Internet Protocol Journal, 3 (December): 14–25.
  22. ^ Opsahl, Tore; Agneessens, Filip; Skvoretz, John (2010). "Node centrality in weighted networks: Generalizing degree and shortest paths". Social Networks 32 (3): 245. doi:10.1016/j.socnet.2010.03.006. 
  23. ^ "Social Network Analysis". Field Manual 3-24: Counterinsurgency. Headquarters, Department of the Army. pp. B–11 – B–12. 
  24. ^ Xu, Guandong et al (2010). Web Mining and Social Networking: Techniques and Applications. Springer. p. 25. ISBN 978-1-4419-7734-2. 
  25. ^ Cohesive.blocking is the R program for computing structural cohesion according to the Moody-White (2003) algorithm. This wiki site provides numerous examples and a tutorial for use with R.
  26. ^ Hanneman, Robert A. & Riddle, Mark (2011). "Concepts and Measures for Basic Network Analysis". The Sage Handbook of Social Network Analysis. SAGE. pp. 346–347. ISBN 978-1-84787-395-8. 
  27. ^ Moody, James, and Douglas R. White (2003). "Structural Cohesion and Embeddedness: A Hierarchical Concept of Social Groups." American Sociological Review 68(1):103–127. Online: (PDF file).
  28. ^ Pattillo, Jeffrey et al (2011). "Clique relaxation models in social network analysis". In Thai, My T. & Pardalos, Panos M. Handbook of Optimization in Complex Networks: Communication and Social Networks. Springer. p. 149. ISBN 978-1-4614-0856-7. 
  29. ^ McGrath, Blythe and Krackhardt. 1997. "The effect of spatial arrangement on judgements and errors in interpreting graphs". Social Networks 19: 223-242.
  30. ^ Bernie Hogan, Juan-Antonio Carrasco and Barry Wellman, "Visualizing Personal Networks: Working with Participant-Aided Sociograms," Field Methods 19 (2), May 2007: 116-144.

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(Sebelumnya) Social network analysis softwareSocial network change detection (Berikutnya)