Qualitative Comparison of Community Detection Algorithms
This work addresses the challenge of accurately comparing community detection methods for researchers in network analysis, though it is incremental as it builds on existing models and methods.
The study tackled the problem of evaluating community detection algorithms by generating networks with a realistic model and applying five algorithms, finding that quantitative performance measures often disagree with qualitative analysis of identified communities.
Community detection is a very active field in complex networks analysis, consisting in identifying groups of nodes more densely interconnected relatively to the rest of the network. The existing algorithms are usually tested and compared on real-world and artificial networks, their performance being assessed through some partition similarity measure. However, artificial networks realism can be questioned, and the appropriateness of those measures is not obvious. In this study, we take advantage of recent advances concerning the characterization of community structures to tackle these questions. We first generate networks thanks to the most realistic model available to date. Their analysis reveals they display only some of the properties observed in real-world community structures. We then apply five community detection algorithms on these networks and find out the performance assessed quantitatively does not necessarily agree with a qualitative analysis of the identified communities. It therefore seems both approaches should be applied to perform a relevant comparison of the algorithms.