Qualitative Comparison of Community Detection Algorithms

TL;DR

This paper compares community detection algorithms using both realistic artificial networks and qualitative analysis, highlighting discrepancies between quantitative performance metrics and qualitative community structure assessments.

Contribution

It introduces a new approach combining realistic network models with qualitative analysis to better evaluate community detection algorithms.

Findings

Quantitative performance does not always match qualitative community structure quality.

Realistic network models reveal properties not captured by artificial networks.

Both quantitative and qualitative analyses are necessary for comprehensive evaluation.

Abstract

Community detection is a very active field in complex networks analysis, consisting in identifying groups of nodes more densely interconnected relatively to the rest of the network. The existing algorithms are usually tested and compared on real-world and artificial networks, their performance being assessed through some partition similarity measure. However, artificial networks realism can be questioned, and the appropriateness of those measures is not obvious. In this study, we take advantage of recent advances concerning the characterization of community structures to tackle these questions. We first generate networks thanks to the most realistic model available to date. Their analysis reveals they display only some of the properties observed in real-world community structures. We then apply five community detection algorithms on these networks and find out the performance assessed quantitatively does not necessarily agree with a qualitative analysis of the identified communities. It therefore seems both approaches should be applied to perform a relevant comparison of the algorithms.