Community detection in networks: A user guide

TL;DR

This paper provides a comprehensive overview of community detection in networks, discussing definitions, methods, validation issues, and practical guidance for researchers in the field.

Contribution

It offers a structured overview of community detection, highlighting challenges, strengths, and weaknesses of existing methods, and provides practical guidance for their application.

Findings

Highlights the ill-defined nature of community detection.

Analyzes strengths and weaknesses of popular algorithms.

Provides directions for effective use of community detection methods.

Abstract

Community detection in networks is one of the most popular topics of modern network science. Communities, or clusters, are usually groups of vertices having higher probability of being connected to each other than to members of other groups, though other patterns are possible. Identifying communities is an ill-defined problem. There are no universal protocols on the fundamental ingredients, like the definition of community itself, nor on other crucial issues, like the validation of algorithms and the comparison of their performances. This has generated a number of confusions and misconceptions, which undermine the progress in the field. We offer a guided tour through the main aspects of the problem. We also point out strengths and weaknesses of popular methods, and give directions to their use.