analysis of a real online social network using semantic web - PowerPoint PPT Presentation

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

social media landscape social web amplifies social network effects

overwhelming flow of social data

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

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

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

semantic social networks http:// sioc-project.org/node/158

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

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]

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

semantic SNA stack exploit the semantic of social networks

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

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

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.

full example closeness centrality through knows and worksWith 1 c , C k length g k x * / * / knows worksWith knows worksWith x E G 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

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

SemSNA an ontology of SNA http://ns.inria.fr/semsna/2009/06/21/voc

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

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

Ipernity

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 {}

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

performances & limits time projections Knows 0.71 s 494 510 Comp rel ( G ) 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 ( ) D rel 1 y 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 Knows Path length <= 2: 14m 50.69s 100 000 to calculate Path length <= 2: 2h 56m 34.13s 1 000 000 C ( b ) Path length <= 2: 7h 19m 15.18s 2 000 000 b rel Favorite Path length <= 2: 5h 33m 18.43s 2 000 000 Friend Path length <= 2: 1m 12.18 s 1 000 000 Path length <= 2: 2m 7.98 s 2 000 000 Family Path length <= 2 : 27.23 s 1 000 000 Path length <= 2 : 2m 9.73 s 3 681 626 Path length <= 3 : 1m 10.71 s 1 000 000 Path length <= 4 : 1m 9.06 s 1 000 000

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

some interpretations existence of a largest component in all sub networks "the effectiveness of the social network at doing its job" [Newman 2003] 70000 knows 60000 favorite 50000 40000 friend 30000 family 20000 10000 message 0 comment number actors size largest component

 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

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

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

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

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

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 2009. 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.