Community Detection in Multiplex Networks

TL;DR

This paper reviews community detection algorithms in multiplex networks, providing a taxonomy, evaluating their effectiveness in identifying ground-truth communities, and analyzing their scalability and similarity in results.

Contribution

It offers a comprehensive taxonomy and experimental evaluation of community detection methods in multiplex networks, aiding in method selection and understanding limitations.

Findings

Methods vary in ability to detect ground-truth communities

Different algorithms produce similar community structures

Scalability of methods varies significantly

Abstract

A multiplex network models different modes of interaction among same-type entities. In this article we provide a taxonomy of community detection algorithms in multiplex networks. We characterize the different algorithms based on various properties and we discuss the type of communities detected by each method. We then provide an extensive experimental evaluation of the reviewed methods to answer three main questions: to what extent the evaluated methods are able to detect ground-truth communities, to what extent different methods produce similar community structures and to what extent the evaluated methods are scalable. One goal of this survey is to help scholars and practitioners to choose the right methods for the data and the task at hand, while also emphasizing when such choice is problematic.