Community detection by resistance distance: automation and benchmark testing

TL;DR

This paper evaluates an unsupervised community detection algorithm based on resistance distance, demonstrating its high accuracy in recovering known communities in benchmark synthetic networks.

Contribution

It introduces automation and accuracy testing for a resistance-distance-based community detection method, validating its effectiveness on benchmark networks.

Findings

The algorithm accurately recovers synthetic communities.

It performs well on Girvan-Newman and Lancichinetti-Fortunato-Radicchi networks.

The method is classified as an accurate community detection approach.

Abstract

Heterogeneity characterises real-world networks, where nodes show a broad range of different topological features. However, nodes also tend to organise into communities -- subsets of nodes that are sparsely inter-connected but are densely intra-connected (more than the network's average connectivity). This means that nodes belonging to the same community are close to each other by some distance measure, such as the resistance distance, which is the effective distance between any pair of nodes considering all possible paths. In this work, we present automation (i.e., unsupervised) and missing accuracy tests for a recently proposed semi-supervised community detection algorithm based on the resistance distance. The accuracy testing involves quantifying our algorithm's performance in terms of recovering known synthetic communities from benchmark networks, where we present results for Girvan-Newman and Lancichinetti-Fortunato-Radicchi networks. Our findings show that our algorithm falls into the class of accurate performers.