analysis of a real online social network using semantic web frameworks Guillaume Erétéo, Michel Buffa, Fabien Gandon, Olivier Corby

Transcription

1 analysis of a real online social network using semantic web frameworks Guillaume Erétéo, Michel Buffa, Fabien Gandon, Olivier Corby

2 social media landscape social web amplifies social network effects

3 overwhelming flow of social data

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

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

6 social network analysis strategic positions and actors degree centrality local attention betweenness centrality reveal broker "A place for good ideas" [Burt, 2004]

7 semantic social networks

8 knows Gérard Fabien Mylène colleague <family>(guillaume)=5 d (guillaume)=3 sibling parent Yvonne Michel sister brother father mother

9 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]

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

11 semantic SNA stack exploit the semantic of social networks

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

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

14 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.

15 full example closeness centrality through knows and workswith 1 C c knows* / work swith k x E G length g knows* / work swith k, x 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

16 e.g. Qualified component Qualified degree Qualified in-degree Qualified diameter Number of geodesics between from and to Number of geodesics between from and to going through b Closenness Centrality Betweenness Centrality

17 SemSNA an ontology of SNA

18 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

19 4 Gérard Mylène 2 Degree colleague Guillaume Yvonne supervisor Michel Fabien Philippe colleague Peter Ivan

20 Ipernity

21 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 {}

22 using real data ipernity.com dataset extracted in RDF actors & relationships family links between actors friend links implicating actors favorite links for actors comments from actors messages exchanged by actors

23 performances & limits Comp rel D rel 1 (G), ( y) Shortest paths used to calculate C b rel (b) Knows 0.71 s Favorite 0.64 s Friend 0.31 s Family 0.03 s Message 1.98 s Comment 9.67 s Knows s Favorite s Friend 1.31 s Family 0.42 s Message s Comment s Knows Path length <= 2: 14m 50.69s Path length <= 2: 2h 56m 34.13s Path length <= 2: 7h 19m 15.18s Favorite Path length <= 2: 5h 33m 18.43s Friend Path length <= 2: 1m s Family Path length <= 2: 2m 7.98 s Path length <= 2 : s Path length <= 2 : 2m 9.73 s Path length <= 3 : 1m s Path length <= 4 : 1m 9.06 s time projections

24 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

25 some interpretations existence of a largest component in all sub networks "the effectiveness of the social network at doing its job" [Newman 2003] know s favorite friend number actors size largest component family message comment

26 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. conclusion

27 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

28 slideshare.net/ereteog holdsaccount twitter.com/ereteog holdsaccount mentorof name organization Guillaume Erétéo manage contribute mentorof answers contribute

29 importing data with SemSNI

30 computer-mediated networks as social networks [Wellman, 2001]

31 Publications International conference Erétéo G., Gandon F., Corby O., Buffa M.: Analysis of a Real Online Social Network Using Semantic Web Frameworks. ISWC2009. Erétéo G., Gandon F., Corby O., Buffa M.: Semantic Social Network Analysis. Web Science Book chapter Guillaume Erétéo, Michel Buffa, Fabien Gandon, Mylène Leitzelman, Freddy Limpens, Peter Sanders: Semantic Social Network Analysis, a concrete case. Handbook of Research on Methods and Techniques for Studying Virtual Communities: Paradigms and Phenomena. A book edited by Ben Kei Daniel, University of Saskatchewan, Canada. scheduled for publication in 2010 by IGI Global National conference Leitzelman M., Erétéo, G., Grohan,, P., Herledan, F., Buffa, M., Gandon, F.: De l'utilité d'un outil de veille d'entreprise de seconde génération. poster in IC2009. Workshop Guillaume Erétéo, Michel Buffa, Fabien Gandon, Mylène Leitzelman, Freddy Limpens Leveraging Social data with Semantics, W3C Workshop on the Future of Social Networking, Barcelona Guillaume Erétéo, Michel Buffa, Fabien Gandon, Patrick Grohan, Mylène Leitzelman, Peter Sander: A State of the Art on Social Network Analysis and its Applications on a Semantic Web, SDoW2008 (Social Data on the Web), workshop at the 7th International Semantic Web Conference.