Detecting the optimal number of communities in complex networks

Zhifang Li, Yanqing Hu, Beishan Xu, Zengru Di and Ying Fan

Physica A: Statistical Mechanics and its Applications, 2012, vol. 391, issue 4, 1770-1776

Abstract: To obtain the optimal number of communities is an important problem in detecting community structures. In this paper, we use the extended measurement of community detecting algorithms to find the optimal community number. Based on the normalized mutual information index, which has been used as a measure for similarity of communities, a statistic Ω(c) is proposed to detect the optimal number of communities. In general, when Ω(c) reaches its local maximum, especially the first one, the corresponding number of communities c is likely to be optimal in community detection. Moreover, the statistic Ω(c) can also measure the significance of community structures in complex networks, which has been paid more attention recently. Numerical and empirical results show that the index Ω(c) is effective in both artificial and real world networks.

Keywords: Complex network; Community structure; Optimal community number (search for similar items in EconPapers)
Date: 2012
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:391:y:2012:i:4:p:1770-1776

DOI: 10.1016/j.physa.2011.06.023

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