Group detection in complex networks: An algorithm and comparison of the state of the art

Lovro Šubelj and Marko Bajec

Physica A: Statistical Mechanics and its Applications, 2014, vol. 397, issue C, 144-156

Abstract: Complex real-world networks commonly reveal characteristic groups of nodes like communities and modules. These are of value in various applications, especially in the case of large social and information networks. However, while numerous community detection techniques have been presented in the literature, approaches for other groups of nodes are relatively rare and often limited in some way. We present a simple propagation-based algorithm for general group detection that requires no a priori knowledge and has near ideal complexity. The main novelty here is that different types of groups are revealed through an adequate hierarchical group refinement procedure. The proposed algorithm is validated on various synthetic and real-world networks, and rigorously compared against twelve other state-of-the-art approaches on group detection, hierarchy discovery and link prediction tasks. The algorithm is comparable to the state of the art in community detection, while superior in general group detection and link prediction. Based on the comparison, we also discuss some prominent directions for future work on group detection in complex networks.

Keywords: Complex networks; Group detection; Hierarchy discovery; Label propagation; Clustering (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:397:y:2014:i:c:p:144-156

DOI: 10.1016/j.physa.2013.12.003

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