Semantic Social Network Analysis Guillaume ERETEO

Social Network Analysis? A science to understand the structure, the interactions and the strategic positions in social networks. Sociograms [Moreno, 1933] What for? –To control information flow –To improve/stimulate communication –To improve network resilience –To trust [Wasserman & Faust 1994] [Scott 2000] [Mika 2007]

Community detection Influences the way information is shared [Coleman 1988] Influences the way actors behave [Burt 2000] Global structure Distribution of actors and activities

Centrality: strategic positions Degree centrality: Local attention beetweenness centrality: reveal broker "A place for good ideas" [Burt 1992] [Burt 2004] Closeness centrality: Capacity to communicate [Freeman 1979]

Critical mass

Balance Theory [Heider 1958]

Computer networks as social networks [Wellman 2001]

 web 2.0 amplifies Network effect !

Semantic social networks Millions of FOAF profiles online

Social tagging SCOT

SNA on the semantic web Rich graph representations reduced to simple untyped graphs in order to apply SNA [Paolillo and Wright 2006] Foaf:knows Foaf:interest

The Semantic SNA Stack

SemSNA an ontology of SNA

Parametrized n-Degree construct construct{ semsna:hasInDegree ?y semsna:hasInDegree _:bO semsna:isDefinedForProperty _:bO semsna:isDefinedForProperty param[type] semsna:hasValue _:bO semsna:hasValue ?indegree param[length] _:b0 semsna:hasDistance param[length] } select ?y count(?x) as ?indegree{ $path ?x $path ?y star(param[type]) filter(match($path, star(param[type]))) pathLength($path) <= param[length] fitler(pathLength($path) <= param[length]) }group by ?y

Most popular manager in a work subnetworks select ?y ?indegree{ ?y rdf:type domain:Manager semsna:hasInDegree ?y semsna:hasInDegree ?z semsna:isDefinedForProperty rel:worksWith ?z semsna:isDefinedForProperty rel:worksWith semsna:hasValue ?z semsna:hasValue ?indegree semsna:hasDistance ?z semsna:hasDistance 2 } order by desc(?indegree)

PREFIX foaf: select ?from ?to ?between $path pathLength($path) as ?length where{ ?from $path ?to param[type] graph $path{?between param[type] ?j} param[type] filter(match($path, star(param[type]), 'sa')) param[type] optional { ?from param[type]::?p ?to } filter(!bound(?p)) filter(?from != ?between) filter(?between != ?to) } group by $path order by ?length Parametrized Shortest paths for betweenness g b (from, to)

PREFIX foaf: PREFIX semsna: add{ ?x semsna:isMemberOf ?uri } select ?x ?y genURI( ) as ?uri from G where { ?x $path ?y param[type] filter(match($path, star(param[type]), 'sa')) } group by any Parametrized Component C b (G)

Current Community detection algorithms Hierarchical algorithms –Agglomerative (based on vertex proximity): [Donetti and Munoz 2004] [Zhou Lipowsky, R. 2004] –Divisive (mostly based on centrality): [Girvan and Newman 2002] [Radicchi et al 2004] Based on heuristic (modularity, randon walk, etc.) [Newman 2004], [Pons and Latapy 2005], [Wu and Huberman 2004]

Toward Semantic Community Detection likes ingredient typemainDish Food subclassOf type

SemSNA an ontology of SNA [Conein 2004] [Wenger 1998]

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