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Clustering coefficient and community structure of bipartite networks

Author

Listed:
  • Zhang, Peng
  • Wang, Jinliang
  • Li, Xiaojia
  • Li, Menghui
  • Di, Zengru
  • Fan, Ying

Abstract

Many real-world networks display natural bipartite structure, where the basic cycle is a square. In this paper, with the similar consideration of standard clustering coefficient in binary networks, a definition of the clustering coefficient for bipartite networks based on the fraction of squares is proposed. In order to detect community structures in bipartite networks, two different edge clustering coefficients LC4 and LC3 of bipartite networks are defined, which are based on squares and triples respectively. With the algorithm of cutting the edge with the least clustering coefficient, communities in artificial and real world networks are identified. The results reveal that investigating bipartite networks based on the original structure can show the detailed properties that is helpful to get deep understanding about the networks.

Suggested Citation

  • Zhang, Peng & Wang, Jinliang & Li, Xiaojia & Li, Menghui & Di, Zengru & Fan, Ying, 2008. "Clustering coefficient and community structure of bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(27), pages 6869-6875.
    • Handle: RePEc:eee:phsmap:v:387:y:2008:i:27:p:6869-6875
    DOI: 10.1016/j.physa.2008.09.006
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    References listed on IDEAS

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    1. Garry Robins & Malcolm Alexander, 2004. "Small Worlds Among Interlocking Directors: Network Structure and Distance in Bipartite Graphs," Computational and Mathematical Organization Theory, Springer, vol. 10(1), pages 69-94, May.
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    8. Sun, Hong-liang & Ch’ng, Eugene & Yong, Xi & Garibaldi, Jonathan M. & See, Simon & Chen, Duan-bing, 2018. "A fast community detection method in bipartite networks by distance dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 108-120.
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    10. Qiao, Jian & Meng, Ying-Ying & Chen, Hsinchun & Huang, Hong-Qiao & Li, Guo-Ying, 2016. "Modeling one-mode projection of bipartite networks by tagging vertex information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 270-279.
    11. Long, Yong-Shang & Jia, Zhen & Wang, Ying-Ying, 2018. "Coarse graining method based on generalized degree in complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 655-665.
    12. Cui, Yaozu & Wang, Xingyuan, 2016. "Detecting one-mode communities in bipartite networks by bipartite clustering triangular," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 307-315.
    13. Gu, Ke & Fan, Ying & Zeng, An & Zhou, Jianlin & Di, Zengru, 2018. "Analysis on large-scale rating systems based on the signed network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 99-109.
    14. Cui, Yaozu & Wang, Xingyuan, 2014. "Uncovering overlapping community structures by the key bi-community and intimate degree in bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 7-14.
    15. Liebig, Jessica & Rao, Asha, 2016. "Predicting item popularity: Analysing local clustering behaviour of users," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 523-531.
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