Enhancing community detection using a network weighting strategy

TL;DR

This paper introduces a network edge weighting strategy based on centrality via random walks to improve the accuracy of existing community detection algorithms on large networks.

Contribution

It proposes a novel pre-processing step that enhances community detection by weighting edges according to their centrality, improving results on large-scale networks.

Findings

Improves accuracy of community detection algorithms

Effective on both synthetic and real-world datasets

Scalable due to efficient edge centrality computation

Abstract

A community within a network is a group of vertices densely connected to each other but less connected to the vertices outside. The problem of detecting communities in large networks plays a key role in a wide range of research areas, e.g. Computer Science, Biology and Sociology. Most of the existing algorithms to find communities count on the topological features of the network and often do not scale well on large, real-life instances. In this article we propose a strategy to enhance existing community detection algorithms by adding a pre-processing step in which edges are weighted according to their centrality w.r.t. the network topology. In our approach, the centrality of an edge reflects its contribute to making arbitrary graph tranversals, i.e., spreading messages over the network, as short as possible. Our strategy is able to effectively complements information about network topology and it can be used as an additional tool to enhance community detection. The computation of edge centralities is carried out by performing multiple random walks of bounded length on the network. Our method makes the computation of edge centralities feasible also on large-scale networks. It has been tested in conjunction with three state-of-the-art community detection algorithms, namely the Louvain method, COPRA and OSLOM. Experimental results show that our method raises the accuracy of existing algorithms both on synthetic and real-life datasets.