Community detection based on the “clumpiness” matrix in complex networks
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.
Year of publication: |
2012
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Authors: | Faqeeh, Ali ; Aghababaei Samani, Keivan |
Published in: |
Physica A: Statistical Mechanics and its Applications. - Elsevier, ISSN 0378-4371. - Vol. 391.2012, 7, p. 2463-2474
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Publisher: |
Elsevier |
Subject: | Real-world networks | Random graphs | Community structure |
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