Overlapping Community Detection in Networks: the State of the Art and Comparative Study

TL;DR

This paper reviews and compares fourteen overlapping community detection algorithms, introduces a new evaluation framework for overlapping nodes, and provides insights into their performance across different network densities.

Contribution

It offers a comprehensive comparison of algorithms and proposes a novel framework for evaluating overlapping node detection in networks.

Findings

SLPA, OSLOM, Game, and COPRA perform well in low-overlap networks.

SLPA and Game are relatively stable in high-overlap networks.

Overlapping nodes in real-world networks are usually few and belong to 2-3 communities.

Abstract

This paper reviews the state of the art in overlapping community detection algorithms, quality measures, and benchmarks. A thorough comparison of different algorithms (a total of fourteen) is provided. In addition to community level evaluation, we propose a framework for evaluating algorithms' ability to detect overlapping nodes, which helps to assess over-detection and under-detection. After considering community level detection performance measured by Normalized Mutual Information, the Omega index, and node level detection performance measured by F-score, we reached the following conclusions. For low overlapping density networks, SLPA, OSLOM, Game and COPRA offer better performance than the other tested algorithms. For networks with high overlapping density and high overlapping diversity, both SLPA and Game provide relatively stable performance. However, test results also suggest that the detection in such networks is still not yet fully resolved. A common feature observed by various algorithms in real-world networks is the relatively small fraction of overlapping nodes (typically less than 30%), each of which belongs to only 2 or 3 communities.