Community detection algorithms: a comparative analysis

TL;DR

This paper evaluates various community detection algorithms on realistic benchmark graphs, revealing that recent methods by Rosvall and Bergstrom, Blondel et al., and Ronhovde and Nussinov perform best in accuracy and efficiency.

Contribution

It provides a comprehensive comparative analysis of community detection algorithms on complex, heterogeneous networks, highlighting the top-performing methods with low computational complexity.

Findings

Recent algorithms outperform older methods in accuracy.

Top algorithms are computationally efficient for large networks.

Benchmark tests reveal strengths and weaknesses of each method.

Abstract

Uncovering the community structure exhibited by real networks is a crucial step towards an understanding of complex systems that goes beyond the local organization of their constituents. Many algorithms have been proposed so far, but none of them has been subjected to strict tests to evaluate their performance. Most of the sporadic tests performed so far involved small networks with known community structure and/or artificial graphs with a simplified structure, which is very uncommon in real systems. Here we test several methods against a recently introduced class of benchmark graphs, with heterogeneous distributions of degree and community size. The methods are also tested against the benchmark by Girvan and Newman and on random graphs. As a result of our analysis, three recent algorithms introduced by Rosvall and Bergstrom, Blondel et al. and Ronhovde and Nussinov, respectively, have an excellent performance, with the additional advantage of low computational complexity, which enables one to analyze large systems.