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Detecting local community structures in complex networks based on local degree central nodes

Author

Listed:
  • Chen, Qiong
  • Wu, Ting-Ting
  • Fang, Ming

Abstract

Detecting local communities in real-world graphs such as large social networks, web graphs, and biological networks has received a great deal of attention because obtaining complete information from a large network is still difficult and unrealistic nowadays. In this paper, we define the term local degree central node whose degree is greater than or equal to the degree of its neighbor nodes. A new method based on the local degree central node to detect the local community is proposed. In our method, the local community is not discovered from the given starting node, but from the local degree central node that is associated with the given starting node. Experiments show that the local central nodes are key nodes of communities in complex networks and the local communities detected by our method have high accuracy. Our algorithm can discover local communities accurately for more nodes and is an effective method to explore community structures of large networks.

Suggested Citation

  • Chen, Qiong & Wu, Ting-Ting & Fang, Ming, 2013. "Detecting local community structures in complex networks based on local degree central nodes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(3), pages 529-537.
  • Handle: RePEc:eee:phsmap:v:392:y:2013:i:3:p:529-537
    DOI: 10.1016/j.physa.2012.09.012
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    Cited by:

    1. Shang, Ronghua & Luo, Shuang & Li, Yangyang & Jiao, Licheng & Stolkin, Rustam, 2015. "Large-scale community detection based on node membership grade and sub-communities integration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 279-294.

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