Different approaches to community detection

TL;DR

This paper reviews various motivations and approaches for community detection in networks, emphasizing the importance of selecting algorithms based on specific research goals rather than just mathematical similarities.

Contribution

It offers a problem-driven classification of community detection methods, clarifying their underlying motivations and guiding their application in practical network science.

Findings

Highlights different motivations for community detection

Classifies algorithms based on research purposes

Identifies open research directions

Abstract

A precise definition of what constitutes a community in networks has remained elusive. Consequently, network scientists have compared community detection algorithms on benchmark networks with a particular form of community structure and classified them based on the mathematical techniques they employ. However, this comparison can be misleading because apparent similarities in their mathematical machinery can disguise different reasons for why we would want to employ community detection in the first place. Here we provide a focused review of these different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different approaches to community detection also delineates the many lines of research and points out open directions and avenues for future research.