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      Permanent link:
      https://www.ias.ac.in/article/fulltext/pram/080/01/0173-0185

    • Keywords

       

      Complex network; community structure; edge betweenness; local random walk.

    • 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.

    • Author Affiliations

       

      Hai-Long Yan1 Ju Xiang2 Xiao-Yu Zhang2 Jun-Feng Fan2 Fang Chane2 Gen-Yi Fu2 Er-Min Guo2 Xin-Guang Hu3 Ke Hu4 Ru-Min Wang1

      1. College of Physics and Electronic Engineering, Xinyang Normal University, Xinyang 464000, Henan, China
      2. Department of Basic Sciences, The First Aeronautical Institute of the Air Force, Xinyang 464000, Henan, China
      3. School of Information Engineering, HuangShan University, HuangShan 245021, Anhui, China
      4. Department of Physics, Xiangtan University, Xiangtan 411105, Hunan, China

    • Dates

       
      Published
      January 2013

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