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Efficient algorithm based on neighborhood overlap for community identification in complex networks

Author

Listed:
  • Li, Kun
  • Gong, Xiaofeng
  • Guan, Shuguang
  • Lai, C.-H.

Abstract

Community structure is an important feature in many real-world networks. Many methods and algorithms for identifying communities have been proposed and have attracted great attention in recent years. In this paper, we present a new approach for discovering the community structure in networks. The novelty is that the algorithm uses the strength of the ties for sorting out nodes into communities. More specifically, we use the principle of weak ties hypothesis to determine to what community the node belongs. The advantages of this method are its simplicity, accuracy, and low computational cost. We demonstrate the effectiveness and efficiency of our algorithm both on real-world networks and on benchmark graphs. We also show that the distribution of link strength can give a general view of the basic structure information of graphs.

Suggested Citation

  • Li, Kun & Gong, Xiaofeng & Guan, Shuguang & Lai, C.-H., 2012. "Efficient algorithm based on neighborhood overlap for community identification in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1788-1796.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:4:p:1788-1796
    DOI: 10.1016/j.physa.2011.09.027
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    Cited by:

    1. Zhenping Li & Xiang-Sun Zhang & Rui-Sheng Wang & Hongwei Liu & Shihua Zhang, 2013. "Discovering Link Communities in Complex Networks by an Integer Programming Model and a Genetic Algorithm," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-10, December.
    2. A. Tabrizi, Shayan & Shakery, Azadeh & Asadpour, Masoud & Abbasi, Maziar & Tavallaie, Mohammad Ali, 2013. "Personalized PageRank Clustering: A graph clustering algorithm based on random walks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(22), pages 5772-5785.

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