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Community detection using global and local structural information

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Abstract

Community detection is of considerable importance for understanding both the structure and function of complex networks. In this paper, we introduced the general procedure of the community detection algorithms using global and local structural information, where the edge betweenness and the local similarity measures respectively based on local random walk dynamics and local cyclic structures were used. The algorithms were tested on artificial and real-world networks. The results clearly show that all the algorithms have excellent performance in the tests and the local similarity measure based on local random walk dynamics is superior to that based on local cyclic structures.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 11147121), the Scientific Research Fund of Education Department of Hunan Province of China (Grant No. 11B128), the project of Basic and Advanced Technology Research of Henan Province (Grant No. 112300410021), the Scientific Research Project of Huangshan University (Grant No. 2011xkj007), and partly by the Doctor Startup Project of Xiangtan University (Grant No. 10QDZ20).

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Authors and Affiliations

  1. College of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, 464000, Henan, China

    HAI-LONG YAN & RU-MIN WANG

  2. Department of Basic Sciences, The First Aeronautical Institute of the Air Force, Xinyang, 464000, Henan, China

    JU XIANG, XIAO-YU ZHANG, JUN-FENG FAN, FANG CHEN, GEN-YI FU & ER-MIN GUO

  3. School of Information Engineering, HuangShan University, HuangShan, 245021, Anhui, China

    XIN-GUANG HU

  4. Department of Physics, Xiangtan University, Xiangtan, 411105, Hunan, China

    KE HU

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  1. HAI-LONG YAN
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Correspondence to JU XIANG.

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YAN, HL., XIANG, J., ZHANG, XY. et al. Community detection using global and local structural information. Pramana - J Phys 80, 173–185 (2013). https://doi.org/10.1007/s12043-012-0359-5

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