Community detection algorithms: A comparative analysis

TLDR

Community detection is essential for understanding complex systems, yet existing algorithms have rarely been rigorously tested on realistic networks, as most evaluations use small or artificial graphs. The study aims to evaluate several community detection methods on a new benchmark graph class with heterogeneous degree and community size distributions. The methods were benchmarked against the new heterogeneous graphs, the Girvan–Newman benchmark, and random graphs to assess performance. Three recent algorithms—Rosvall and Bergstrom, Blondel et al., and Ronhovde and Nussinov—exhibited excellent performance with low computational complexity, enabling analysis of large systems.

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.