Vertex-centred Method to Detect Communities in Evolving Networks

TL;DR

This paper presents DynLOCNeSs, a vertex-centred algorithm for detecting evolving communities in networks, utilizing a new preference measure CWCN for improved efficiency and accuracy in identifying community dynamics.

Contribution

The paper introduces DynLOCNeSs, a novel parallelizable algorithm based on vertex preferences, and proposes CWCN, a new preference measure optimized for dynamic community detection.

Findings

CWCN outperforms existing measures in efficiency

DynLOCNeSs effectively detects community evolution patterns

Algorithm is suitable for parallel implementation

Abstract

Finding communities in evolving networks is a difficult task and raises issues different from the classic static detection case. We introduce an approach based on the recent vertex-centred paradigm. The proposed algorithm, named DynLOCNeSs, detects communities by scanning and evaluating each vertex neighbourhood, which can be done independently in a parallel way. It is done by means of a preference measure, using these preferences to handle community changes. We also introduce a new vertex neighbourhood preference measure, CWCN, more efficient than current existing ones in the considered context. Experimental results show the relevance of this measure and the ability of the proposed approach to detect classical community evolution patterns such as grow-shrink and merge-split.