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- 1. Guillaume Erétéo, Michel Buffa, Fabien Gandon, Olivier Corby
- 2. computer-mediated networks as social networks [Wellman, 2001]
- 3. social media landscape <ul><li>social web amplifies social network effects </li></ul>
- 4. overwhelming flow of social data monitoring notifying animating consulting
- 5. social network analysis <ul><li>proposes graph algorithms to characterize the structure of a social network, strategic positions, and networking activities </li></ul>
- 6. social network analysis <ul><li>global metrics and structure </li></ul>community detection distribution of actors and activities density and diameter cohesion of the network
- 7. social network analysis <ul><li>strategic positions and actors </li></ul>degree centrality local attention
- 8. social network analysis <ul><li>strategic positions and actors </li></ul>betweenness centrality reveal broker "A place for good ideas" [Burt, 2004]
- 9. semantic social networks http:// sioc-project.org/node/158
- 10. (guillaume)=5 Gérard Fabien Mylène Michel Yvonne father sister mother colleague colleague d
- 11. Gérard Fabien Mylène Michel Yvonne father sister mother colleague colleague <family> d (guillaume)= 3 parent sibling mother father brother sister colleague knows
- 12. but … <ul><li>SPARQL is not expressive enough to meet SNA requirements for global metric querying of social networks (density, betweenness centrality, etc.). </li></ul>[San Martin & Gutierrez 2009]
- 13. classic SNA on semantic web <ul><li>rich graph representations reduced to simple </li></ul><ul><li>untyped graphs </li></ul>[Paolillo & Wright, 2006] foaf:knows foaf:interest
- 14. semantic SNA stack <ul><li>exploit the semantic of social networks </li></ul>
- 15. SPARQL extensions <ul><li>CORESE semantic search engine implementing semantic web languages using graph-based representations </li></ul>
- 16. grouping results <ul><li>number of followers of a twitter user </li></ul><ul><li>select ?y count( ?x ) as ?indegree where{ </li></ul><ul><li>?x twitter:follow ?y </li></ul><ul><li>} group by ?y </li></ul>
- 17. path extraction <ul><li>people knowing, knowing, (...) colleagues of someone </li></ul><ul><li>?x sa (foaf:knows*/rel:worksWith)::$path ?y </li></ul><ul><li>filter(pathLength($path) <= 4) </li></ul><ul><li>Regular expression operators are: / (sequence) ; | (or) ; * (0 or more) ; ? (optional) ; ! (not) </li></ul><ul><li>Path characteristics: i to allow inverse properties, s to retrieve only one shortest path, sa to retrieve all shortest paths. </li></ul>
- 18. full example <ul><li>closeness centrality through knows and worksWith </li></ul><ul><li>select distinct ?y ?to </li></ul><ul><li>pathLength($path) as ?length </li></ul><ul><li>(1/sum(?length)) as ?centrality </li></ul><ul><li>where{ </li></ul><ul><li>?y s (foaf:knows*/rel:worksWith)::$path ?to </li></ul><ul><li>}group by ?y </li></ul>
- 19. Qualified component Qualified in-degree Qualified diameter Closenness Centrality Betweenness Centrality Number of geodesics between from and to Qualified degree Number of geodesics between from and to going through b
- 20. SemSNA an ontology of SNA <ul><li>http://ns.inria.fr/semsna/2009/06/21/voc </li></ul>
- 21. add to the RDF graph <ul><li>saving the computed degrees for incremental calculations </li></ul><ul><li>CONSTRUCT </li></ul><ul><li>{ </li></ul><ul><li>?y semsna: hasSNAConcept _:b0 </li></ul><ul><li>_:b0 rdf:type semsna: Degree </li></ul><ul><li>_:b0 semsna: hasValue ?degree </li></ul><ul><li>_:b0 semsna: isDefinedForProperty rel:family </li></ul><ul><li>} </li></ul><ul><li>SELECT ?y count(?x) as ?degree where </li></ul><ul><li>{ </li></ul><ul><li>{ ?x rel:family ?y } </li></ul><ul><li>UNION </li></ul><ul><li>{ ?y rel:family ?x } </li></ul><ul><li>}group by ?y </li></ul>
- 22. sister mother supervisor hasSNAConcept isDefinedForProperty hasValue colleague colleague father hasCentralityDistance colleague colleague supervisor 4 Philippe 2 colleague supervisor Degree Guillaume Gérard Fabien Mylène Michel Yvonne Ivan Peter
- 23. Ipernity
- 24. using real data <ul><li>extracting a real dataset from a relational database </li></ul><ul><li>construct { ?person1 rel:friendOf ?person2 } </li></ul><ul><li>select sql(<server>, <driver>, <user>, <pwd>, select user1_id, user2_id from relations where rel = 1 ') as (?person1 , ?person2 ) </li></ul><ul><li>where {} </li></ul>
- 25. importing data with SemSNI <ul><li>http://ns.inria.fr/semsni/ </li></ul>
- 26. using real data <ul><li>ipernity.com dataset extracted in RDF 61 937 actors & 494 510 relationships </li></ul><ul><li>18 771 family links between 8 047 actors </li></ul><ul><li>136 311 friend links implicating 17 441 actors </li></ul><ul><li>339 428 favorite links for 61 425 actors </li></ul><ul><li>2 874 170 comments from 7 627 actors </li></ul><ul><li>795 949 messages exchanged by 22 500 actors </li></ul>
- 27. performances & limits time projections Knows 0.71 s 494 510 Favorite 0.64 s 339 428 Friend 0.31 s 136 311 Family 0.03 s 18 771 Message 1.98 s 795 949 Comment 9.67 s 2 874 170 Knows 20.59 s 989 020 Favorite 18.73 s 678 856 Friend 1.31 s 272 622 Family 0.42 s 37 542 Message 16.03 s 1 591 898 Comment 28.98 s 5 748 340 Shortest paths used to calculate Knows Path length <= 2: 14m 50.69s Path length <= 2: 2h 56m 34.13s Path length <= 2: 7h 19m 15.18s 100 000 1 000 000 2 000 000 Favorite Path length <= 2: 5h 33m 18.43s 2 000 000 Friend Path length <= 2: 1m 12.18 s Path length <= 2: 2m 7.98 s 1 000 000 2 000 000 Family Path length <= 2 : 27.23 s Path length <= 2 : 2m 9.73 s Path length <= 3 : 1m 10.71 s Path length <= 4 : 1m 9.06 s 1 000 000 3 681 626 1 000 000 1 000 000
- 28. some interpretations <ul><li>validated with managers of ipernity.com </li></ul><ul><li>friendOf , favorite , message , comment small diameter, high density </li></ul><ul><li>family as expected: large diameter, low density </li></ul><ul><li>favorite : highly centralized around Ipernity animator. </li></ul><ul><li>friendOf , family , message , comment : power law of degrees and betweenness centralities, different strategic actors </li></ul><ul><li>knows : analyze all relations using subsumption </li></ul>
- 29. some interpretations <ul><li>existence of a largest component in all sub networks </li></ul><ul><li>"the effectiveness of the social network at doing its job" [Newman 2003] </li></ul>
- 30. conclusion <ul><li>directed typed graph structure of RDF/S well suited to represent social knowledge & socially produced metadata spanning both internet and intranet networks. </li></ul><ul><li>definition of SNA operators in SPARQL (using extensions and OWL Lite entailment) enable to exploit the semantic structure of social data. </li></ul><ul><li>SemSNA organize and structure social data. </li></ul>
- 31. perspectives <ul><li>semantic based community detection algorithm </li></ul><ul><li>SemSNA Ontology </li></ul><ul><ul><li>extract complex SNA features reusing past results </li></ul></ul><ul><ul><li>support iterative or parallel approaches in the computations </li></ul></ul><ul><li>a semantic SNA to foster a semantic intranet of people </li></ul><ul><ul><li>structure overwhelming flows of corporate social data </li></ul></ul><ul><ul><li>foster and strengthen social interactions </li></ul></ul><ul><ul><li>efficient access to the social capital [Krebs, 2008] built through online collaboration </li></ul></ul>http://twitter.com/isicil
- 32. name Guillaume Erétéo holdsAccount organization mentorOf mentorOf holdsAccount manage contribute contribute answers twitter.com/ereteog slideshare.net/ereteog

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http://www-sop.inria.fr/members/Fabien.Gandon/docs/ISWC2009_ereteo_et_al.pdf