Detecting community structure in networks using edge prediction methods

TL;DR

This paper introduces a novel approach that enhances community detection in networks by leveraging vertex similarity measures from edge prediction, leading to more effective identification of community structures.

Contribution

It proposes a new method combining vertex similarity with existing community detection algorithms, improving their effectiveness.

Findings

Improved community detection accuracy over basic algorithms

Vertex similarity measures can enhance community structure identification

The method outperforms traditional community detection approaches

Abstract

Community detection and edge prediction are both forms of link mining: they are concerned with discovering the relations between vertices in networks. Some of the vertex similarity measures used in edge prediction are closely related to the concept of community structure. We use this insight to propose a novel method for improving existing community detection algorithms by using a simple vertex similarity measure. We show that this new strategy can be more effective in detecting communities than the basic community detection algorithms.