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Social Network Analysis. Christopher McCarty University of Florida. Outline. Social network concepts Social network data collection Social network metrics UCINET using your data Egonet using your data. Recommended Texts.

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Social Network Analysis


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    1. Social Network Analysis Christopher McCarty University of Florida

    2. Outline • Social network concepts • Social network data collection • Social network metrics • UCINET using your data • Egonet using your data

    3. Recommended Texts • Scott, John. 2000. Social Network Analysis: A Handbook. Newbury Park, CA: Sage Publications. • Wasserman, Stanley, and Katherine Faust. 1994. Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press. • Freeman, Linton C. 2004. The Development of Social Network Analysis: A Study in the Sociology of Science. North Charleston, S.C. : BookSurge, 2004

    4. Recommended Web Sites • www.insna.org -- International Network for Social Network Analysis • http://faculty.ucr.edu/~hanneman/nettext/ -- Tutorial for UCINET/Netdraw • http://www.redes-sociales.net/ (Spanish social network listserv)

    5. Recommended articles • Stephen P. Borgatti, Ajay Mehra, Daniel J. Brass, Giuseppe Labianca (2009) Network Analysis in the Social Sciences, Science 323: 892-895 • Peter Marsden (1990) Network data and measurement Annual Review of Sociology 16:435-463

    6. Recommended Software • Ucinet(Whole networks) • (www.analytictech.com) ($40 for students, $150 for faculty) • E-net (Batch processing of ego networks) • (www.analytictech.com) • Pajek (Whole networks, large networks) • (http://vlado.fmf.uni-lj.si/pub/networks/pajek/) • Egonet(Personal networks) • (http://sourceforge.net/projects/egonet/) • Vennmaker (Personal networks) • Siena (Network modeling, longitudinal) • http://stat.gamma.rug.nl/siena.html • Network Genie (Online network data collection) • https://secure.networkgenie.com/

    7. Recommended Journals • Social Networks • Connections • Journal of Social Structure • American Journal of Sociology, Social Science and Medicine, Journal of Mathematical Sociology, Organization Science, Social Forces, Gerontologist

    8. Recommended Conference

    9. Social Network Analysis is the study of the pattern of interaction between actors

    10. Examples of actors and their networks • Children in a preschool • Employees in an office • Customers of AT&T mobile phone service • NGOs working in the Amazon • Companies in the Fortune 500 • Countries in the European Union • Baboons in a troupe • Organisms in the Chesapeake Bay • Web sites around the world

    11. Is SNA just a set of tools or is it a theoretical approach?See: http://www.insna.org/PDF/Sunbelt/3_KeynotePDF.pdf • Social Capital, Structural Holes, Simmelian ties • Strong and weak ties • Small world • Scale-free networks • Network diffusion

    12. Social Capital • “…the ability of actors to secure benefits by virtue of membership in social networks or other social structures” (Portes 1998) • Alejandro Portes (1998) SOCIAL CAPITAL: Its Origins and Applications in Modern Sociology, Annual Review of Sociology 1998. 24:1–24 • Ron Burt (2004) Structural Holes and Good Ideas, American Journal of Sociology 110: 349–399

    13. David Krackhardt (1999) The ties that torture: Simmelian tie analysis in organizations, Research in the Sociology of Organizations 16: 183-210

    14. Structure matters (but is not always enough) • In some contexts structure is a necessary, but not sufficient, condition for social capital • The most beneficial structural position may depend on the topic • Job seeking • Social support • Social network evaluation and intervention does not always mean you should connect the dots • Facebook model is to suggest connections • Sometimes there are reasons for not connecting

    15. Strong and weak ties • The most beneficial tie may not always be the strong ones • Strong ties are often connected to each other and are therefore sources of redundant information • Mark Granovetter (1973) The strength of weak ties American Journal of Sociology 78-1361-1381.

    16. Small world phenomenon • Being linked, seemingly by chance, through someone via a friend or acquaintance • Stanley Milgram (1967)The Small World Psychology Today 2:60–67. • Peter D. Killworth, H. Russell Bernard and Christopher McCarty (1984) Measuring Patterns of Acquaintance Current Anthropology 25:381-397 • Duncan Watts and Steven Strogatz (1998) Collective dynamics of 'small-world' networks Nature 393 (6684): 409–10

    17. Scale Free Networks • Scale free refers to the power law structure of networks as the number of actors increases • Networks tend to form hubs • Entry of physicists into SNA • Albert-LászlóBarabási and Réka Albert (1999) Emergence of scaling in random network. Science, 286:509-512.

    18. Network Diffusion • Network structures can often aid or impede the flow of information and the adoption of innovations • Diffusion of innovation is the basis for peer to peer network interventions • Coleman, James, Elihu Katz, and Herbert Menzel. 1957. The diffusion of innovation among physicians. Sociometry. 20:253-270. • Valente, Thomas W. 1996 “Social network thresholds in the diffusion of innovations” Social Networks 18:69-89. • Klovdahl, A. S. (1985). Social networks and the spread of infectious diseases: The AIDS example. Social Science Medicine, 21(11), 1203-1216.

    19. Whole (Complete, Sociocentric) Network Analysis Focus on interaction within a group Boundary defines social space Collect data from members of a group about their ties to other group members Personal (Egocentric) Network Analysis Focus on effects of network on individual attitudes, behaviors and conditions Use attributes of personal network to represent social context Collect data from respondent (ego) about interactions with network members (alters) Two kinds of Social Network Analysis

    20. Sociocentric Network Data From Graduate Anthropology Course

    21. Three network components • Beth is most degree central • Amber is most between central • Thomas and Kent are structurally equivalent • Removal of David maximizes network fragmentation

    22. Boundary definition • Boundaries can be defined: • Geographically (a village) • Socially (an organization) • Through connections (snowball) • The idea is that actors within the boundary are in some way affected by their social position • This excludes the effects from those outside the boundary

    23. Missing data • In whole networks responses by others about non-respondents can capture structure • 70% will in many cases be enough • GueorgiKossinets (2006) Effects of missing data in social networks. Social Networks 28: 247–268. • Costenbader, E. & Valente, T. W. (2004). The stability of centrality when networks are sampled. Social Networks.

    24. Whole (Complete, Sociocentric) Network Analysis Focus on interaction within a group Boundary defines social space Collect data from members of a group about their ties to other group members Personal (Egocentric) Network Analysis Focus on effects of network on individual attitudes, behaviors and conditions Use attributes of personal network to represent social context Collect data from respondent (ego) about interactions with network members (alters) Two kinds of Social Network Analysis

    25. Personal network interview • Identify a population • Select a sample of respondents • Ask questions about respondent • Elicit network members • Ask questions about each network member • Ask respondent to evaluate ties between network members

    26. Personal Network CompositionAlter summary file

    27. Personal network composition variables • Proportion of personal network that are women • Average age of network alters • Proportion of strong ties • Average number of years knowing alters

    28. Personal Network StructureAlter adjacency matrix

    29. Personal network structural variables • Average degree centrality (density) • Average closeness centrality • Average betweenness centrality • Core/periphery • Number of components • Number of isolates

    30. Boundary definition for personal networks • Facebook • West Africa and Asia • Time • First grade teacher • Require mutual recognition • Book author • Living • Dead relative (Genogram) • Jesus

    31. Two categories of data collection • One mode data • Actors by actors • Examples of one mode data collection • Survey; E-mail; Telephone calls; Observation of interaction • Two mode data • Actors by events • Examples of two mode data collection • Attendance at parties, meetings, funerals; Purchase of items; Reading particular authors

    32. Kinds of data

    33. Example 1One mode - Whole network

    34. University – Water Management District interaction • Objective: Understand structure of interaction between academic and applied scientists • Procedure 1: Bound universities by those published in journals in St. Johns Water Management District library in 2008 • Procedure 2: Bound WMD by employee e-mails on web sites • Procedure 3: Web survey with letter and $1 incentive to all 705 actors • Response: 332 completed surveys

    35. Visualization of University-WMD network

    36. Example 2One mode - Personal network

    37. Acculturation study • Objective: Test social network compositional and structural variables as proxies for acculturation • Procedure 1: Interviewed 535 migrants in Barcelona and New York City • Procedure 2: Each respondent listed 45 network alters • Procedure 3: Respondents provided twelve pieces of information about each alter • Procedure 4: Respondents evaluated all 990 unique alter-alter ties

    38. Visualization of the networks of two sistersLabel = Country of origin, Size = Closeness, Color = Skin color, Shape = Smoking Status • Mercedes is a 19-year-old second generation Gambian woman in Barcelona • She is Muslim and lives with her parents and 8 brothers and sisters • She goes to school, works and stays home caring for her siblings. She does not smoke or drink. • Laura is a 22-year-old second generation Gambian woman in Barcelona • She is Muslim and lives with her parents and 8 brothers and sisters • She works, but does not like to stay home. She smokes and drinks and goes to parties on weekends.

    39. Example 3Two mode - Whole network

    40. Southern women • Objective: To understand the network structure of the debutante network in a Southern town in the 1940s • Procedure: Observe which of the 14 annual balls each of the 18 women attended

    41. Two mode data matrix

    42. Visualization of two mode data

    43. Example 4Two mode - personal network

    44. Relation categories in Thailand • Objective: Discover mutually exclusive and exhaustive categories in a language for how people know each other to be used on a network scale-up survey instrument

    45. Procedure 1: Twenty one respondents freelist in Thai ways that people know each other

    46. Procedure 2: Twenty one respondents list 30 people they know and apply 26 most frequently occurring categories

    47. Affiliation from all respondents

    48. Graph of relationship between knowing categories

    49. Social network metrics • Group level • Density • Components • Isolates • Cliques • Centralization • Degree • Closeness • Betweenness • Factions • Core-periphery • Node level • Centrality • Degree • Indegree • Outdegree • Closeness • Betweenness • Visualization • Netdraw • Mage • QAP • Statistical metrics • MDS • Cluster analysis