Evidential Link Prediction Based on Group Information

Sabrine Mallek (1) , Imen Boukhris (1) , Zied Elouedi (1) , Eric Lefevre (2)

1 LARODEC - Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus
2 LGI2A - Laboratoire de Génie Informatique et d'Automatique de l'Artois

Link prediction has become a common way to infer new associations among actors in social networks. Most existing methods focus on the local and global information neglecting the implication of the actors in social groups. Further, the prediction process is characterized by a high complexity and uncertainty. In order to address these problems, we firstly introduce a new evidential weighted version of the social networks graph-based model that encapsulates the uncertainty at the edges level using the belief function framework. Secondly, we use this graph-based model to provide a novel approach for link prediction that takes into consideration both groups information and uncertainty in social networks. The performance of the method is experimented on a real world social network with group information and shows interesting results.

Mots clés: social network analysis; link prediction; uncertain social network; group information; belief function theory

Domaines: Intelligence artificielle [cs.AI]

Sabrine Mallek, Imen Boukhris, Zied Elouedi, Eric Lefevre. Evidential Link Prediction Based on Group Information. International Conference on Mining Intelligence and Knowledge Exploration, MIKE'2015, Dec 2015, Hyderabad, India. pp.482-492, ⟨10.1007/978-3-319-26832-3_45⟩. ⟨hal-03649503⟩