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Clustering coefficient and community structure of bipartite networks

Author

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  • 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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    5. Ramadiah, Amanah & Caccioli, Fabio & Fricke, Daniel, 2019. "Reconstructing and stress testing credit networks," LSE Research Online Documents on Economics 118938, London School of Economics and Political Science, LSE Library.
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    8. Fessina, Massimiliano & Zaccaria, Andrea & Cimini, Giulio & Squartini, Tiziano, 2024. "Pattern-detection in the global automotive industry: A manufacturer-supplier-product network analysis," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
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    11. Zhang, Dawei & Xie, Fuding & Zhang, Yong & Dong, Fangyan & Hirota, Kaoru, 2010. "Fuzzy analysis of community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(22), pages 5319-5327.
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    14. Neelu Chaudhary & Hardeo Kumar Thakur & Rinky Dwivedi, 2022. "An ensemble model to optimize modularity in dynamic bipartite networks," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2248-2260, October.
    15. Moradi-Jamei, Behnaz & Shakeri, Heman & Poggi-Corradini, Pietro & Higgins, Michael J., 2021. "A new method for quantifying network cyclic structure to improve community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
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