Evidential Link Prediction in Uncertain Social Networks Based on Node Attributes

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

The design of an efficient link prediction method is still an open hot issue that has been addressed mostly through topological properties in recent years. Yet, other relevant information such as the node attributes may inform the link prediction task and enhance performances. This paper presents a novel framework for link prediction that combines node attributes and structural properties. Furthermore, the proposed method handles uncertainty that characterizes social network noisy and missing data by embracing the general framework of the belief function theory. An experimental evaluation on real world social network data shows that attribute information improves further the prediction results.

Mots clés: social network analysis; link prediction; uncertain social network; belief function theory; node attributes; structural properties

Domaines: Intelligence artificielle [cs.AI]

Sabrine Mallek, Imen Boukhris, Zied Elouedi, Eric Lefevre. Evidential Link Prediction in Uncertain Social Networks Based on Node Attributes. International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE'2017, Jun 2017, Arras, France. pp.595-601, ⟨10.1007/978-3-319-60042-0_65⟩. ⟨hal-03649435⟩