Comparative Evaluation of Community Detection Algorithms: A Topological Approach

TL;DR

This paper conducts a comprehensive comparison of community detection algorithms using both traditional partition-based metrics and topological properties, revealing that these evaluation methods are complementary and provide different insights.

Contribution

It introduces a dual-evaluation framework combining community structure measures and topological properties for a more complete assessment of detection algorithms.

Findings

Performance metrics do not always align with topological correctness.

Both evaluation approaches are necessary for thorough assessment.

Artificial networks used to mimic real-world systems.

Abstract

Community detection is one of the most active fields in complex networks analysis, due to its potential value in practical applications. Many works inspired by different paradigms are devoted to the development of algorithmic solutions allowing to reveal the network structure in such cohesive subgroups. Comparative studies reported in the literature usually rely on a performance measure considering the community structure as a partition (Rand Index, Normalized Mutual information, etc.). However, this type of comparison neglects the topological properties of the communities. In this article, we present a comprehensive comparative study of a representative set of community detection methods, in which we adopt both types of evaluation. Community-oriented topological measures are used to qualify the communities and evaluate their deviation from the reference structure. In order to mimic real-world systems, we use artificially generated realistic networks. It turns out there is no equivalence between both approaches: a high performance does not necessarily correspond to correct topological properties, and vice-versa. They can therefore be considered as complementary, and we recommend applying both of them in order to perform a complete and accurate assessment.