A divisive spectral method for network community detection

TL;DR

This paper introduces a divisive spectral method that combines network sparsification with spectral bisection to improve the accuracy and clarity of community detection in complex networks.

Contribution

It proposes a novel divisive spectral approach that pre-processes networks with sparsification before applying spectral bisection, enhancing community detection performance.

Findings

The method produces clearer community boundaries.

It outperforms existing community detection algorithms.

High accuracy in identifying community structures.

Abstract

Community detection is a fundamental problem in the domain of complex-network analysis. It has received great attention, and many community detection methods have been proposed in the last decade. In this paper, we propose a divisive spectral method for identifying community structures from networks, which utilizes a sparsification operation to pre-process the networks first, and then uses a repeated bisection spectral algorithm to partition the networks into communities. The sparsification operation makes the community boundaries more clearer and more sharper, so that the repeated spectral bisection algorithm extract high-quality community structures accurately from the sparsified networks. Experiments show that the combination of network sparsification and spectral bisection algorithm is highly successful, the proposed method is more effective in detecting community structures from networks than the others.