The Basics of Social Network Analysis
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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.

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 The Basics of Social Network Analysis Presentation Transcript

  • The Basics of Social Network Analysis
    • Adriana Berlanga & Rory Sie
    • LN SNA Seminar series, November 15th 2011
  • Outline
    • History
    • Examples
    • Network Data
    • Analysis
  • History Social Network Analysis Psychology Anthropology
  • 1930s: Jacob Moreno http://institutomomento.wordpress.com sociogram
  • 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”
  • 1920s: Warner and Mayo Mayo Warner Hawthorne http://administracion1enlinea.blogspot.com http://www.wolframalpha.com effect focus on relationships
  • 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
  • Social Networks
    • 1950s: ‘network’ (Barnes, Bott, Nadel)
    • 1960s: Density and reachability (Mitchell)
    A B C D E A-B-C-E
  • Mark Granovetter
    • Getting a Job (1974)
    • Strength of weak ties (1983)
    ://www.stanford.edu/dept/soc/people/mgranovetter/
  • History Social Network Analysis Psychology Anthropology Hawthorne Networks Graph theory Sociogram Graph theory Strength of weak ties
  • Examples centrality = power (Krackhardt, 1990) ‘ broker’ (Burt, 2004)
  • Why?
    • encourages re-use and prevent re-invention
    • increase knowledge sharing
    • discover effective and efficient (sub)communities
    • reduce burden on experts/teachers
    adapted from Liebowitz, 2005
  • Data collection
  • Ego network ego network but.... self-perceived ask for connections ask connections if they are connected alter alter
  • Snowball method but.... self-perceived ask for connections ask connections for their connections until you reach a stopping criterion
  • Complete networks monitor email traffic or monitor tweets
  • Data storage Adjacency matrix (R, UCINET)
  • Data storage
    • <?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;>
    • <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;/>
      • </edge>
        • </graph>
    GML/ XGMML (Cytoscape, Gephi)
  • Data storage Pajek network (Pajek, UCINET)
  • Analysis: network
    • Density
    • Connectivity k
    • Centralization
    A D C B E
  • Analysis: community
    • Clique
    A D C B E F every person in a clique can be reached within 1 step
  • Analysis: community
    • N-clique
    A D C B E F every person can be reached within n steps. ABCF is a 2-clique
  • Analysis: community A D C B E F Faction http://www.physorg.com/news/2011-01-mathematical-groups-factions.html
  • 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
  • Summary
    • Why?
      • encourages re-use
      • reduce burden on teacher
      • discover effective and efficient (sub)communities
    • Data collection
    • Which technique?
  • References
    • Brandes, U. (1994). A Faster Algorithm for Betweenness Centrality. Journal of Mathematical Sociology , 25 (2), 163-177.
    • Burt, R. S. (2004). Structural Holes and Good Ideas. American Journal of Sociology , 110 (2), 349-399. doi:10.1086/421787
    • Cartwright, D., & Harary, F. (1977). A Graph Theoretic Approach to the Investigation of System-Environment Relationships. Journal of Mathematical Sociology , 5 , 87-111.
    • Granovetter, M. (1974). Getting A Job: A Study of Contacts and Careers. Cambridge, Massachusetts.
    • Krackhardt, D. (1990). Assessing the Political Landscape : Structure, Cognition, and Power in Organizations. Administrative Science Quarterly , 35 (2), 342-369.
    • 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
    • Scott, J. (2000). Social Network Analysis: a Handbook (p. 208). SAGE Publications, Inc.
    • Factions video. http://www.physorg.com/news/2011-01-mathematical-groups-factions.html
  • Questions?
    • [email_address]
    • http://www.open.ou.nl/rse
    • openrory, maisonpoublon
    • Rory Sie
    • openrse
    • http://nl.linkedin.com/in/rorysie
    • thebigbangrory.blogspot.com
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