A State of the Art on Social Network Analysis and its Applications on a Semantic Web

1. A State of the Art on Social Network Analysis and its Applications on a Semantic Web Guillaume ERETEO, Michel Buffa, Fabien Gandon, Patrick Grohan, Mylène Leitzelman, Peter Sander

2. What's going on? • Social network analysis (SNA) : • Indices, Algorithms • Social network analysis and the web • Web "1.0"  Web 2.0  Semantic web • Semantic Social Network Analysis

3. Paul Peter friend of friend of Jack 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 communication • To improve network resilience • To trust

4. Community detection • Global structure • Distribution of actors and activities Influences the way information is shared [Coleman 1988] Influences the way actors behave [Burt 2000]

5. Community detection • 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]

6. Centrality: strategic positions [Freeman 1979] Degree centrality: Local attention Closeness centrality: Capacity to communicate beetweenness centrality: reveal broker "A place for good ideas" [Burt 1992] [Burt 2004]

7. Betweenness Centrality • Exact computation O(n.m) • [Newman 2001] [Brandes 2001] • Approximation : • [Brandes et al 2007] [Bader et al 2007] [Geisberg et al 2008] • Parallel algorithms • [Bader et al 2006] [Santos et al 2006]

8. Computer networks as social networks [Wellman 2001] • Asynchonous and synchronous conversation : mail, IRC, chat • [Tyler et al 2003] • Hyperlink structure of homepages • [Adamic and Adar 2003] • Cooccurence of names in web pages • [Kautz el al 1997][Mika 2005] • [Matsuo et al 2006][Jin et al 2007]

9. Network effect amplified by web 2.0

10. Semantic social networks Millions of FOAF profiles online http://sioc-project.org/node/158

11. SNA on the semantic web [Paolillo and Wright 2006] • Rich graph representations are always reduced to simple untyped graphs in order to apply SNA Foaf:knows Foaf:interest [Golbeck et al 2003] [Golbeck and Rothstein 2008]

12. Semantic SNA

13. Ndegree with SPARQL and the semantic engine CORESE construct{ ?y semsna:hasInDegree _:bO _:bO semsna:isDefinedForProperty rel:worksWith _:bO semsna:hasValue ?indegree _:b0 semsna:hasDistance 2 } select ?y count(?x) as ?indegree{ ?x $path ?y filter(match($path, star(foaf:knows))) fitler(pathLength($path)<= 2) }group by ?y filter(match($path, star(rel:worksWith)))

14. Select the most popular actors in a work subnetworks select ?x ?indegree{ ?x semsna:hasInDegree ?z ?z semsna:isDefinedForProperty rel:worksWith ?z semsna:hasValue ?indegree ?z semsna:hasDistance 2 } order by desc(?indegree)

15. Pre-process betweenness centrality detect paths between all pairs of indirectly connected resources and their intermediaries PREFIX foaf: <http://xmlns.com/foaf/0.1/> select ?from ?to ?between $path pathLength($path) as ?length where{ ?from $path ?to graph $path{?between foaf:knows ?j} filter(match($path, star(foaf:knows))) filter(?from != ?to) filter(?from != ?between) filter(?between != ?to) optional { ?from foaf:knows::?p ?to } filter(!bound(?p)) } group by $path order by ?length

16. Thank you for your attention Questions ?