Evidential Communities for Complex Networks

Kuang Zhou (1, 2) , Arnaud Martin (2) , Quan Pan (1)

1 School of Automation
2 DRUID - Declarative & Reliable management of Uncertain, user-generated Interlinked Data

Community detection is of great importance for understand-ing graph structure in social networks. The communities in real-world networks are often overlapped, i.e. some nodes may be a member of multiple clusters. How to uncover the overlapping communities/clusters in a complex network is a general problem in data mining of network data sets. In this paper, a novel algorithm to identify overlapping communi-ties in complex networks by a combination of an evidential modularity function, a spectral mapping method and evidential c-means clustering is devised. Experimental results indicate that this detection approach can take advantage of the theory of belief functions, and preforms good both at detecting community structure and determining the appropri-ate number of clusters. Moreover, the credal partition obtained by the proposed method could give us a deeper insight into the graph structure.

Mots clés: Overlapping com-munities; Credal partition:; Evidential modularity; Evidential c-means

Domaines: Intelligence artificielle [cs.AI]

Kuang Zhou, Arnaud Martin, Quan Pan. Evidential Communities for Complex Networks. 15th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Jul 2014, Montpellier, France. pp.557 - 566, ⟨10.1007/978-3-319-08795-5_57⟩. ⟨hal-01101172⟩