Finding community structure using the ordered random graph model

TL;DR

This paper introduces a new ordering algorithm based on maximum-likelihood estimation of the ordered random graph model, improving the visualization and detection of community structures in networks.

Contribution

The paper presents a novel algorithm for ordering adjacency matrices that enhances community structure visualization compared to existing methods.

Findings

The proposed method more clearly reveals community structures in adjacency matrices.

The algorithm outperforms classical ordering algorithms in identifying communities.

Visualization of network features is improved using the new ordering technique.

Abstract

Visualization of the adjacency matrix enables us to capture macroscopic features of a network when the matrix elements are aligned properly. Community structure, a network consisting of several densely connected components, is a particularly important feature, and the structure can be identified through the adjacency matrix when it is close to a block-diagonal form. However, classical ordering algorithms for matrices fail to align matrix elements such that the community structure is visible. In this study, we propose an ordering algorithm based on the maximum-likelihood estimate of the ordered random graph model. We show that the proposed method allows us to more clearly identify community structures than the existing ordering algorithms.