Qualitative Comparison of Community Detection Algorithms

Abstract

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.

DICTAP 2011, The International Conference on Digital Information and Communication Technology and its Applications, Dijon : France (2011)

References in corpus (7)

• Fast unfolding of communities in large networks
• Uncovering the overlapping community structure of complex networks in nature and society
• Maps of random walks on complex networks reveal community structure
• Benchmark graphs for testing community detection algorithms
• Resolution limit in community detection
• Characterizing the community structure of complex networks
• Evaluating Local Community Methods in Networks

Cited by in corpus (3)

• Consensus clustering in complex networks
• Surprise maximization reveals the community structure of complex networks
• An Empirical Study of the Relation Between Community Structure and Transitivity