Detecting communities via edge Random Walk Centrality

TL;DR

This paper introduces a new community detection method based on edge Random Walk Centrality, which is efficient and tested on various networks, including the Indian Railway network for robustness improvements.

Contribution

The paper adapts node RWC to edges via line graphs, enabling efficient community detection without recalculating centrality after each step.

Findings

Effective community detection across multiple network types

Algorithm outperforms existing methods in speed and accuracy

Identifies critical edges to enhance network robustness

Abstract

Herein we present a novel approach of identifying community structures in complex networks. We propose the usage of the Random Walk Centrality (RWC), first introduced by Noh and Rieger [Phys. Rev. Lett. 92.11 (2004): 118701]. We adapt this node centrality metric to an edge centrality metric by applying it to the line graph of a given network. A crucial feature of our algorithm is the needlessness of recalculating the centrality metric after each step, in contrast to most community detection algorithms. We test our algorithm on a wide variety of standard networks, and compare them with pre-existing algorithms. As a predictive application, we analyze the Indian Railway network for robustness and connectedness, and propose edges which would make the system even sturdier.