  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.
