Adjusting from disjoint to overlapping community detection of complex networks

Xiaohua Wang, Licheng Jiao and Jianshe Wu

Physica A: Statistical Mechanics and its Applications, 2009, vol. 388, issue 24, 5045-5056

Abstract: The investigation of community structures is one of the most important problems in the field of complex networks and has countless applications in different disciplines: biology, computer, social sciences, etc. Many community detection algorithms have been developed in various fields recently. The vast majority of these algorithms only find disjoint communities; however, in many real-world networks communities often overlap to some extent. In this paper, we propose an efficient method for adjusting these classical algorithms to match the requirement for discovering overlapping communities in complex networks, which is based on a local definition of community strength. The method can in principle be applied with any clustering algorithm. Tests on a set of computer generated and real-world networks give excellent results. In particular, we show that the method can also allow one to availably analyze the problem of unstable nodes in community detection, which is very helpful for understanding the structural properties of the networks correctly and comprehensively.

Keywords: Complex networks; Community detection; Disjoint community; Overlapping community (search for similar items in EconPapers)
Date: 2009
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:388:y:2009:i:24:p:5045-5056

DOI: 10.1016/j.physa.2009.08.032

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