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The Basics of Social Network Analysis

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An introduction in the world of Social Network Analysis and a view on how this may help learning networks. History, data collection and several analysis techniques are shown.

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The Basics of Social Network Analysis

  1. 1. The Basics of Social Network Analysis <ul><li>Adriana Berlanga & Rory Sie </li></ul><ul><li>LN SNA Seminar series, November 15th 2011 </li></ul>
  2. 2. Outline <ul><li>History </li></ul><ul><li>Examples </li></ul><ul><li>Network Data </li></ul><ul><li>Analysis </li></ul>
  3. 3. History Social Network Analysis Psychology Anthropology
  4. 4. 1930s: Jacob Moreno http://institutomomento.wordpress.com sociogram
  5. 5. 1950s: Cartwright and Harary Dorwin Cartwright http://www.rcgd.isr.umich.edu http://www.ur.umich.edu Frank Harary + - - A B C “ any balanced graph can be divided into two cohesive sub-groups that are in conflict with each other”
  6. 6. 1920s: Warner and Mayo Mayo Warner Hawthorne http://administracion1enlinea.blogspot.com http://www.wolframalpha.com effect focus on relationships
  7. 7. 1920s: Warner and Mayo Mayo Warner http://administracion1enlinea.blogspot.com http://www.wolframalpha.com adapted from Scott, 2000 cliques every person is separated by only one step
  8. 8. Social Networks <ul><li>1950s: ‘network’ (Barnes, Bott, Nadel) </li></ul><ul><li>1960s: Density and reachability (Mitchell) </li></ul>A B C D E A-B-C-E
  9. 9. Mark Granovetter <ul><li>Getting a Job (1974) </li></ul><ul><li>Strength of weak ties (1983) </li></ul>://www.stanford.edu/dept/soc/people/mgranovetter/
  10. 10. History Social Network Analysis Psychology Anthropology Hawthorne Networks Graph theory Sociogram Graph theory Strength of weak ties
  11. 11. Examples centrality = power (Krackhardt, 1990) ‘ broker’ (Burt, 2004)
  12. 12. Why? <ul><li>encourages re-use and prevent re-invention </li></ul><ul><li>increase knowledge sharing </li></ul><ul><li>discover effective and efficient (sub)communities </li></ul><ul><li>reduce burden on experts/teachers </li></ul>adapted from Liebowitz, 2005
  13. 13. Data collection
  14. 14. Ego network ego network but.... self-perceived ask for connections ask connections if they are connected alter alter
  15. 15. Snowball method but.... self-perceived ask for connections ask connections for their connections until you reach a stopping criterion
  16. 16. Complete networks monitor email traffic or monitor tweets
  17. 17. Data storage Adjacency matrix (R, UCINET)
  18. 18. Data storage <ul><li><?xml version=&quot;1.0&quot; encoding=&quot;UTF-8&quot; standalone=&quot;yes&quot;?><graph label=&quot;PLN for ID &quot; directed=&quot;1&quot;> </li></ul><ul><li><node id=&quot; n26 &quot; label=&quot;n26&quot;><att type=&quot;string&quot; name=&quot;PeerName&quot; value=&quot; Rory Sie &quot;/></node><node id=&quot; n27 &quot; label=&quot;n27&quot;><att type=&quot;string&quot; name=&quot;PeerName&quot; value=&quot; Adriana Berlanga &quot;/></node><edge id=&quot;e0&quot; label=&quot;e0&quot; source=&quot; n26 &quot; target=&quot; n27 &quot;><att type=&quot;string&quot; name=&quot;interaction&quot; value=&quot;colleague&quot;&quot;/> </li></ul><ul><ul><li></edge> </li></ul></ul><ul><ul><ul><li></graph> </li></ul></ul></ul>GML/ XGMML (Cytoscape, Gephi)
  19. 19. Data storage Pajek network (Pajek, UCINET)
  20. 20. Analysis: network <ul><li>Density </li></ul><ul><li>Connectivity k </li></ul><ul><li>Centralization </li></ul>A D C B E
  21. 21. Analysis: community <ul><li>Clique </li></ul>A D C B E F every person in a clique can be reached within 1 step
  22. 22. Analysis: community <ul><li>N-clique </li></ul>A D C B E F every person can be reached within n steps. ABCF is a 2-clique
  23. 23. Analysis: community A D C B E F Faction http://www.physorg.com/news/2011-01-mathematical-groups-factions.html
  24. 24. Analysis: individual A D C B E F G H Betweenness network is dependent on C Degree G is very popular 12 12 14 17 17 18 19 19 Closeness C and G can easily reach others
  25. 25. Summary <ul><li>Why? </li></ul><ul><ul><li>encourages re-use </li></ul></ul><ul><ul><li>reduce burden on teacher </li></ul></ul><ul><ul><li>discover effective and efficient (sub)communities </li></ul></ul><ul><li>Data collection </li></ul><ul><li>Which technique? </li></ul>
  26. 26. References <ul><li>Brandes, U. (1994). A Faster Algorithm for Betweenness Centrality. Journal of Mathematical Sociology , 25 (2), 163-177. </li></ul><ul><li>Burt, R. S. (2004). Structural Holes and Good Ideas. American Journal of Sociology , 110 (2), 349-399. doi:10.1086/421787 </li></ul><ul><li>Cartwright, D., & Harary, F. (1977). A Graph Theoretic Approach to the Investigation of System-Environment Relationships. Journal of Mathematical Sociology , 5 , 87-111. </li></ul><ul><li>Granovetter, M. (1974). Getting A Job: A Study of Contacts and Careers. Cambridge, Massachusetts. </li></ul><ul><li>Krackhardt, D. (1990). Assessing the Political Landscape : Structure, Cognition, and Power in Organizations. Administrative Science Quarterly , 35 (2), 342-369. </li></ul><ul><li>Liebowitz, J. (2005). Linking social network analysis with the analytic hierarchy process for knowledge mapping in organizations. Journal of Knowledge Management , 9 (1), 76-86. doi:10.1108/13673270510582974 </li></ul><ul><li>Scott, J. (2000). Social Network Analysis: a Handbook (p. 208). SAGE Publications, Inc. </li></ul><ul><li>Factions video. http://www.physorg.com/news/2011-01-mathematical-groups-factions.html </li></ul>
  27. 27. Questions? <ul><li>[email_address] </li></ul><ul><li>http://www.open.ou.nl/rse </li></ul><ul><li>openrory, maisonpoublon </li></ul><ul><li>Rory Sie </li></ul><ul><li>openrse </li></ul><ul><li>http://nl.linkedin.com/in/rorysie </li></ul><ul><li>thebigbangrory.blogspot.com </li></ul>
  28. 28. NOW: PLN Drawing <ul><li>http://bit.ly/sfo47G </li></ul><ul><ul><li>register </li></ul></ul><ul><ul><li>add the people you learn from! </li></ul></ul><ul><ul><li>15 minutes </li></ul></ul>