Enhancing community detection by local structural information

TL;DR

This paper investigates how local structural information, captured by various similarity measures, can enhance community detection in complex networks through an edge-reweighting strategy, showing that local measures significantly improve detection accuracy.

Contribution

The study introduces a method of using local similarity measures for edge reweighting to improve community detection, highlighting the importance of network-specific measures.

Findings

Local similarity measures improve community detection accuracy.

The effectiveness of measures depends on network type.

Edge-reweighting enhances existing detection methods.

Abstract

Many real-world networks such as the gene networks, protein-protein interaction networks and metabolic networks exhibit community structures, meaning the existence of groups of densely connected vertices in the networks. Many local similarity measures in the networks are closely related to the concept of the community structures, and may have positive effect on community detection in the networks. Here, various local similarity measures are used to extract the local structural information and then are applied to community detection in the networks by using the edge-reweighting strategy. The effect of the local similarity measures on community detection is carefully investigated and compared in various networks. The experimental results show that the local similarity measures are crucial to the improvement for the community detection methods, while the positive effect of the local similarity measures is closely related to the networks under study and the applied community detection methods.