Detecting community structure via the maximal sub-graphs and belonging degrees in complex networks

Yaozu Cui, Xingyuan Wang and Justine Eustace

Physica A: Statistical Mechanics and its Applications, 2014, vol. 416, issue C, 198-207

Abstract: Community structure is a common phenomenon in complex networks, and it has been shown that some communities in complex networks often overlap each other. So in this paper we propose a new algorithm to detect overlapping community structure in complex networks. To identify the overlapping community structure, our algorithm firstly extracts fully connected sub-graphs which are maximal sub-graphs from original networks. Then two maximal sub-graphs having the key pair-vertices can be merged into a new larger sub-graph using some belonging degree functions. Furthermore we extend the modularity function to evaluate the proposed algorithm. In addition, overlapping nodes between communities are founded successfully. Finally we report the comparison between the modularity and the computational complexity of the proposed algorithm with some other existing algorithms. The experimental results show that the proposed algorithm gives satisfactory results.

Keywords: Complex networks; Community structure; Maximal sub-graph; Belonging degree (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:416:y:2014:i:c:p:198-207

DOI: 10.1016/j.physa.2014.08.050

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