Point of view based clustering of socio-semantic networks

Juan David Cruz Gomez (1, 2) , Cécile Bothorel (1, 2) , François Poulet (3)

1 LUSSI - Département Logique des Usages, Sciences sociales et Sciences de l'Information
2 Lab-STICC_TB_CID_DECIDE
3 TEXMEX - Multimedia content-based indexing

Classic algorithms for community detection in social networks use the structural information to identify groups in the social network, i.e., how clusters are formed according to the topology of the relationships. However, these methods do not take into account any semantic information which could guide the clustering process, and which may add elements to do further analyses. The method we propose, uses in a conjoint way, the semantic information from the social network, represented by the point of view, and its structural information. This is, by the combination of the relationships, expressed by the edges on one hand, and the implicit relations deduced from the semantic information on the other hand.

Keywords: Communities detection; Clustering; Machine learning; Semantic networks; Social network analysis

Domains: Machine Learning [cs.LG]; Human-Computer Interaction [cs.HC]

Juan David Cruz Gomez, Cécile Bothorel, François Poulet. Point of view based clustering of socio-semantic networks. 2011, pp.2. ⟨hal-00609268⟩