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

Guillaume Erétéo, Michel Buffa, Fabien Gandon, Olivier Corby

analysis of a real

• nline social network

using semantic web frameworks

social media landscape

social web amplifies social network effects

• verwhelming 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 betweenness centrality reveal broker

semantic social networks

http://sioc-project.org/node/158

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

d(guillaume)=3 guillaume

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

x k g length k C ,

e.g.

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

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 Philippe colleague 2 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(<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

Knows 0.71 s 494 510 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 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 to calculate Knows Path length <= 2: 14m 50.69s Path length <= 2: 2h 56m 34.13s Path length <= 2: 7h 19m 15.18s 100 000 1 000 000 2 000 000 Favorite Path length <= 2: 5h 33m 18.43s 2 000 000 Friend Path length <= 2: 1m 12.18 s Path length <= 2: 2m 7.98 s 1 000 000 2 000 000 Family Path length <= 2 : 27.23 s Path length <= 2 : 2m 9.73 s Path length <= 3 : 1m 10.71 s Path length <= 4 : 1m 9.06 s 1 000 000 3 681 626 1 000 000 1 000 000

) (G Comp rel

) (

,

y D rel 1 ) (b C

rel b

time projections

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]

10000 20000 30000 40000 50000 60000 70000 number actors size largest component knows favorite friend family message comment

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
• rganize 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

http://twitter.com/isicil

name Guillaume Erétéo holdsAccount

• rganization

mentorOf mentorOf holdsAccount manage contribute contribute answers twitter.com/ereteog slideshare.net/ereteog

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.