Communities and beyond: mesoscopic analysis of a large social network with complementary methods

TL;DR

This paper evaluates the effectiveness of three community detection methods on a large real-world social network derived from mobile phone data, revealing their strengths, limitations, and hierarchical relationships among detected communities.

Contribution

It provides a comprehensive analysis of community detection performance on large social networks, highlighting the potential for mesoscale network analysis beyond dense community identification.

Findings

Methods detect meaningful communities but with limitations.

Hierarchical relationships exist between communities detected by different methods.

Community detection can reveal mesoscale network structures.

Abstract

Community detection methods have so far been tested mostly on small empirical networks and on synthetic benchmarks. Much less is known about their performance on large real-world networks, which nonetheless are a significant target for application. We analyze the performance of three state-of-the-art community detection methods by using them to identify communities in a large social network constructed from mobile phone call records. We find that all methods detect communities that are meaningful in some respects but fall short in others, and that there often is a hierarchical relationship between communities detected by different methods. Our results suggest that community detection methods could be useful in studying the general mesoscale structure of networks, as opposed to only trying to identify dense structures.