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

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 community detection distribution of actors and activities density and diameter cohesion of the network

social network analysis strategic positions and actors degree centrality local attention

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

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

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

parent sibling motherfather brothersister colleague knows Gérard Fabien Mylène Michel Yvonne father sister mother colleague d(guillaume)=3

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 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 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

SemSNA an ontology of SNA

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

sister mother supervisor hasSNAConcept isDefinedForProperty hasValue 4 4 colleague father Philippe hasCentralityDistance colleague 2 2 supervisor colleague supervisor Degree Guillaume Gérard Fabien Mylène Michel Yvonne Ivan Peter

Ipernity

using real data extracting a real dataset from a relational database construct { ?person1 rel:friendOf ?person2 } select sql(,,,, select user1_id, user2_id from relations where rel = 1 ') as (?person1, ?person2 ) where {}

importing data with SemSNI

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

performances & limits Knows0.71 s Favorite0.64 s Friend0.31 s Family0.03 s Message1.98 s Comment9.67 s Knows20.59 s Favorite18.73 s Friend1.31 s Family0.42 s Message16.03 s Comment28.98 s Shortest paths used to calculate KnowsPath length <= 2: 14m 50.69s Path length <= 2: 2h 56m 34.13s Path length <= 2: 7h 19m 15.18s FavoritePath length <= 2: 5h 33m 18.43s FriendPath length <= 2: 1m s Path length <= 2: 2m 7.98 s FamilyPath length <= 2 : s Path length <= 2 : 2m 9.73 s Path length <= 3 : 1m s Path length <= 4 : 1m 9.06 s timeprojections

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]

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.

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

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