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

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  • Faqeeh, Ali
  • Aghababaei Samani, Keivan

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.

Suggested Citation

  • Faqeeh, Ali & Aghababaei Samani, Keivan, 2012. "Community detection based on the “clumpiness” matrix in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2463-2474.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:7:p:2463-2474
    DOI: 10.1016/j.physa.2011.12.017
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    Citations

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    Cited by:

    1. Shang, Ronghua & Bai, Jing & Jiao, Licheng & Jin, Chao, 2013. "Community detection based on modularity and an improved genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1215-1231.
    2. Ji, Junzhong & Song, Xiangjing & Liu, Chunnian & Zhang, Xiuzhen, 2013. "Ant colony clustering with fitness perception and pheromone diffusion for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(15), pages 3260-3272.
    3. Hedayatifar, L. & Hassanibesheli, F. & Shirazi, A.H. & Vasheghani Farahani, S. & Jafari, G.R., 2017. "Pseudo paths towards minimum energy states in network dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 109-116.

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