# Community structure: A comparative evaluation of community detection methods

## TL;DR

This paper provides a comprehensive empirical comparison of various community detection methods across multiple network types, analyzing their performance, community structures, and suitability for different applications.

## Contribution

It offers a detailed evaluation of community detection algorithms, including their computational efficiency and community characteristics, aiding practitioners in selecting appropriate methods.

## Key findings

- Different methods produce varying community size distributions.
- Optimization schemes influence community detection outcomes.
- Partitioning strategies differ significantly across 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.