Evidential Communities for Complex Networks

TL;DR

This paper introduces a novel method combining evidential modularity, spectral mapping, and evidential c-means clustering to detect overlapping communities in complex networks, providing deeper insights into graph structures.

Contribution

It presents a new algorithm that leverages belief function theory for improved detection of overlapping communities and determining the number of clusters.

Findings

Effective detection of overlapping communities.

Accurate estimation of the number of clusters.

Enhanced understanding of graph structure through credal partitions.

Abstract

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.