Guillaume Erétéo, Michel Buffa, Fabien Gandon, Olivier Corby

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

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

social media landscape social web amplifies social network effects

overwhelming flow of social data consulting notifying animating monitoring

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

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

social network analysis strategic positions and actors degree centrality local attention

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

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

Fabien Mylène Gérard colleague father sister d (guillaume)=5 colleague mother Michel Yvonne

Fabien Mylène Gérard knows colleague father sister d (guillaume)=3 <family> colleague colleague mother Michel parent sibling Yvonne father mother sister 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 [Paolillo & Wright, 2006] rich graph representations reduced to simple untyped graphs foaf:knows foaf:interest

semantic SNA stack exploit the semantic of social networks

SPARQL extensions CORESE semantic search engine implementing semantic web languagesusing 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, sato retrieve all shortest paths.

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

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 hasValue hasCentralityDistance 2 Degree sister father colleague isDefinedForProperty hasSNAConcept Yvonne mother Guillaume supervisor supervisor supervisor Michel Fabien colleague colleague colleague 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 {}

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

using real data ipernity.com dataset extracted in RDF61 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

some interpretations validated with managers of ipernity.com • friendOf, favorite, message, commentsmall 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]

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. • SemSNAorganize and structure social data.

perspectives http://twitter.com/isicil • 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

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

Guillaume Erétéo, Michel Buffa, Fabien Gandon, Olivier Corby