Evidential community detection based on density peaks

TL;DR

This paper introduces EDPC, a novel evidential community detection algorithm based on density peaks that uses belief functions to better capture uncertainty in community structures within graph data.

Contribution

The paper proposes a new evidential community detection method utilizing density peak concepts and belief functions, enhancing uncertainty representation in community analysis.

Findings

Effective on real-world networks

Outperforms existing methods in accuracy

Captures uncertainty in community memberships

Abstract

Credal partitions in the framework of belief functions can give us a better understanding of the analyzed data set. In order to find credal community structure in graph data sets, in this paper, we propose a novel evidential community detection algorithm based on density peaks (EDPC). Two new metrics, the local density $\rho$ and the minimum dissimi-larity $\delta$, are first defined for each node in the graph. Then the nodes with both higher $\rho$ and $\delta$ values are identified as community centers. Finally, the remaing nodes are assigned with corresponding community labels through a simple two-step evidential label propagation strategy. The membership of each node is described in the form of basic belief assignments , which can well express the uncertainty included in the community structure of the graph. The experiments demonstrate the effectiveness of the proposed method on real-world networks.