Title: Community structure: A comparative evaluation of community detection methods,
Abstract: Discovering community structure in complex networks is a mature field since a
tremendous number of community detection methods have been introduced in the
literature. Nevertheless, it is still very challenging for practioners to
determine which method would be suitable to get insights into the structural
information of the networks they study. Many recent efforts have been devoted
to investigating various quality scores of the community structure, but the
problem of distinguishing between different types of communities is still open.
In this paper, we propose a comparative, extensive and empirical study to
investigate what types of communities many state-of-the-art and well-known
community detection methods are producing. Specifically, we provide
comprehensive analyses on computation time, community size distribution, a
comparative evaluation of methods according to their optimisation schemes as
well as a comparison of their partioning strategy through validation metrics.
We process our analyses on a very large corpus of hundreds of networks from
five different network categories and propose ways to classify community
detection methods, helping a potential user to navigate the complex landscape
of community detection.