The document discusses computer-mediated networks as social networks, focusing on social network analysis (SNA) using graph algorithms to understand network structures and actor positions. It presents the challenges of querying social networks using SPARQL, and suggests using semantic models to enhance the analysis of social data. The authors propose the development of a semantic SNA ontology that could improve community detection and feature extraction for social networks.

1. Guillaume Erétéo, Michel Buffa, Fabien Gandon, Olivier Corby

2. computer-mediated networks as social networks [Wellman, 2001]

3. social media landscape social web amplifies social network effects

4. overwhelming flow of social data monitoring notifying animating consulting

5. social network analysis proposes graph algorithms to characterize the structure of a social network, strategic positions, and networking activities

6. social network analysis global metrics and structure community detection distribution of actors and activities density and diameter cohesion of the network

7. social network analysis strategic positions and actors degree centrality local attention

8. social network analysis strategic positions and actors 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 … SPARQL is not expressive enough to meet SNA requirements for global metric querying of social networks (density, betweenness centrality, etc.). [San Martin & Gutierrez 2009]

13. classic SNA on semantic web rich graph representations reduced to simple untyped graphs [Paolillo & Wright, 2006] foaf:knows foaf:interest

14. semantic SNA stack exploit the semantic of social networks

15. SPARQL extensions CORESE semantic search engine implementing semantic web languages using graph-based representations

16. grouping results number of followers of a twitter user select ?y count( ?x ) as ?indegree where{ ?x twitter:follow ?y } group by ?y

17. path extraction people knowing, knowing, (...) colleagues of someone ?x sa (foaf:knows*/rel:worksWith)::$path ?y filter(pathLength($path) <= 4) Regular expression operators are: / (sequence) ; | (or) ; * (0 or more) ; ? (optional) ; ! (not) Path characteristics: i to allow inverse properties, s to retrieve only one shortest path, sa to retrieve all shortest paths.

18. full example closeness centrality through knows and worksWith select distinct ?y ?to pathLength($path) as ?length (1/sum(?length)) as ?centrality where{ ?y s (foaf:knows*/rel:worksWith)::$path ?to }group by ?y

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 http://ns.inria.fr/semsna/2009/06/21/voc

21. add to the RDF graph saving the computed degrees for incremental calculations CONSTRUCT { ?y semsna: hasSNAConcept _:b0 _:b0 rdf:type semsna: Degree _:b0 semsna: hasValue ?degree _:b0 semsna: isDefinedForProperty rel:family } SELECT ?y count(?x) as ?degree where { { ?x rel:family ?y } UNION { ?y rel:family ?x } }group by ?y

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 extracting a real dataset from a relational database construct { ?person1 rel:friendOf ?person2 } select sql(<server>, <driver>, <user>, <pwd>, select user1_id, user2_id from relations where rel = 1 ') as (?person1 , ?person2 ) where {}

25. importing data with SemSNI http://ns.inria.fr/semsni/

26. using real data ipernity.com dataset extracted in RDF 61 937 actors & 494 510 relationships 18 771 family links between 8 047 actors 136 311 friend links implicating 17 441 actors 339 428 favorite links for 61 425 actors 2 874 170 comments from 7 627 actors 795 949 messages exchanged by 22 500 actors

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 validated with managers of ipernity.com friendOf , favorite , message , comment small diameter, high density family as expected: large diameter, low density favorite : highly centralized around Ipernity animator. friendOf , family , message , comment : power law of degrees and betweenness centralities, different strategic actors knows : analyze all relations using subsumption

29. some interpretations existence of a largest component in all sub networks &quot;the effectiveness of the social network at doing its job&quot; [Newman 2003]

30. conclusion directed typed graph structure of RDF/S well suited to represent social knowledge & socially produced metadata spanning both internet and intranet networks. definition of SNA operators in SPARQL (using extensions and OWL Lite entailment) enable to exploit the semantic structure of social data. SemSNA organize and structure social data.

31. perspectives semantic based community detection algorithm SemSNA Ontology extract complex SNA features reusing past results support iterative or parallel approaches in the computations a semantic SNA to foster a semantic intranet of people structure overwhelming flows of corporate social data foster and strengthen social interactions efficient access to the social capital [Krebs, 2008] built through online collaboration http://twitter.com/isicil

32. name Guillaume Erétéo holdsAccount organization mentorOf mentorOf holdsAccount manage contribute contribute answers twitter.com/ereteog slideshare.net/ereteog