Community detection based on the “clumpiness” matrix in complex networks

Ali Faqeeh and Keivan Aghababaei Samani

Physica A: Statistical Mechanics and its Applications, 2012, vol. 391, issue 7, 2463-2474

Abstract: The “clumpiness” matrix of a network is used to develop a method to identify its community structure. A “projection space” is constructed from the eigenvectors of the clumpiness matrix and a border line is defined using some kind of angular distance in this space. The community structure of the network is identified using this borderline and/or hierarchical clustering methods. The performance of our algorithm is tested on some computer-generated and real-world networks. The accuracy of the results is checked using normalized mutual information. The effect of community size heterogeneity on the accuracy of the method is also discussed.

Keywords: Real-world networks; Random graphs; Community structure (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:391:y:2012:i:7:p:2463-2474

DOI: 10.1016/j.physa.2011.12.017

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