semantic social network analysis

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semantic social network analysis

  1. 1. Semantic Social Network Analysis Guillaume ERETEO
  2. 2. Social Network Analysis? [Wasserman & Faust 1994] [Scott 2000] [Mika 2007] • A science to understand the structure, the interactions and the strategic positions in social networks. • Sociograms [Moreno, 1933] • What for? – To control information flow – To improve/stimulate communication – To improve network resilience – To trust
  3. 3. Community detection • Global structure • Distribution of actors and activities Influences the way information is shared Influences the way actors behave [Coleman 1988] [Burt 2000]
  4. 4. Centrality: strategic positions [Freeman 1979] Degree centrality: Local attention Closeness centrality: Capacity to communicate Community detection: Distribution of actors and activities beetweenness centrality: reveal broker "A place for good ideas" [Burt 1992] [Burt 2004]
  5. 5. Critical mass
  6. 6. Balance Theory [Heider 1958]
  7. 7. Computer networks as social networks [Wellman 2001]
  8. 8.  web 2.0 amplifies Network effect !
  9. 9. Semantic social networks Millions of FOAF profiles online http://sioc-project.org/node/158
  10. 10. Social tagging SCOT
  11. 11. SNA on the semantic web [Paolillo and Wright 2006] Foaf:knows Foaf:interest Rich graph representations reduced to simple untyped graphs in order to apply SNA
  12. 12. The Semantic SNA Stack
  13. 13. Semantic paths in social graphs mainDish type type ingredient likes subclassOf Food
  14. 14. Fabien Mylène e knows Gérard colleagu e r ist fat s he r colleague d < familly > ( guillaume )c olle agu m e ot he sibling parent r Michel Yvonne sister brother father mother
  15. 15. Fabien Mylène e knows Gérard colleagu e r ist fat s he r colleague d < familly > ( guillaume )c = 3 olle agu m e ot he sibling parent r Michel Yvonne sister brother father mother
  16. 16. Closeness centrality Cc<type>(y) select ?y ?to pathLength($path) as ?length sum(?length) as ?centrality where{ ?y $path ?to filter(match($path, star(param[type]), param[type] 'sa')) } group by ?y
  17. 17. Parametrized Component C<type>(G) add{ ?x semsna:isMemberOf ?uri } select ?x ?y genURI(<myorg>) as ?uri from G where { ?x $path ?y filter(match($path, star(param[type]), 'sa')) param[type] } group by any
  18. 18. SemSNA an ontology of SNA
  19. 19. [Wenger 1998] [Conein 2004] SemSNA an ontology of SNA
  20. 20. construct{ ?y semsna:hasInDegree _:bO _:bO semsna:isDefinedForProperty param[type] _:bO semsna:hasValue ?indegree _:b0 semsna:hasDistance param[length] } select ?y count(?x) as ?indegree{ ?x $path ?y filter(match($path, star(param[type]))) fitler(pathLength($path) <= param[length]) param[length] }group by ?y Parametrized n-Degree
  21. 21. Most popular manager in a work subnetworks select ?y ?indegree{ ?y rdf:type domain:Manager ?y semsna:hasInDegree ?z ?z semsna:isDefinedForProperty rel:worksWith ?z semsna:hasValue ?indegree ?z semsna:hasDistance 2 } order by desc(?indegree)
  22. 22. Current Community detection algorithms • Hierarchical algorithms – Agglomerative (based on vertex proximity): • [Donetti and Munoz 2004] [Zhou Lipowsky, R. 2004] – Divisive (mostly based on centrality): • [Girvan and Newman 2002] [Radicchi et al 2004] • Based on heuristic (modularity, randon walk, etc.) • [Newman 2004], [Pons and Latapy 2005], [Wu and Huberman 2004]
  23. 23. Web sémantique label #tag27 #tag92 label hasTag Semantic web hasTag #bk81 #bk34 hasBookmark hasBookmark ShareInterest #Guigui #Fabien MentorOf MentorOf Collaborate #Michel
  24. 24. organization organization organization name Guillaume Erétéo mentorOf mail guillaume.ereteo@orange-ftgoup.com manage contribute answers mentorOf contribute

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