Communities and beyond: mesoscopic analysis of a large social network with complementary methods

Abstract

Community detection methods have so far been tested mostly on small empirical networks and on synthetic benchmarks. Much less is known about their performance on large real-world networks, which nonetheless are a significant target for application. We analyze the performance of three state-of-the-art community detection methods by using them to identify communities in a large social network constructed from mobile phone call records. We find that all methods detect communities that are meaningful in some respects but fall short in others, and that there often is a hierarchical relationship between communities detected by different methods. Our results suggest that community detection methods could be useful in studying the general mesoscale structure of networks, as opposed to only trying to identify dense structures.

11 pages, 10 figures. V2: typos corrected, one sentence added. V3: revised version, Appendix added. V4: final published version

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Cited by in corpus (6)

• A Large-Scale Community Structure Analysis In Facebook
• Self-similar scaling of density in complex real-world networks
• Criterions for locally dense subgraphs
• A survey of results on mobile phone datasets analysis
• Computational Human Dynamics
• Overlapping Community Structure in Co-authorship Networks: a Case Study