Community detection based on "clumpiness" matrix in complex networks

TL;DR

This paper introduces a novel community detection method in complex networks using the eigenvectors of a 'clumpiness' matrix, leveraging a projection space and hierarchical clustering to identify communities.

Contribution

The paper presents a new community detection algorithm based on the 'clumpiness' matrix and eigenvector analysis, offering an alternative to existing methods.

Findings

Effective in computer-generated networks

Validated on real-world networks

Accuracy assessed with normalized mutual information

Abstract

The "clumpiness" matrix of a network is used to develop a method to identify its community structure. A "projection space" is constructed from the eigenvectors of the clumpiness matrix and a border line is defined using some kind of angular distance in this space. The community structure of the network is identified using this borderline and/or hierarchical clustering methods. The performance of our algorithm is tested on some computer-generated and real-world networks. The accuracy of the results is checked using normalized mutual information. The effect of community size heterogeneity on the accuracy of the method is also discussed.