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The analysis and dissimilarity comparison of community structure

Author

Listed:
  • Zhang, Peng
  • Li, Menghui
  • Wu, Jinshan
  • Di, Zengru
  • Fan, Ying

Abstract

Based on a database of collaboration recording in econophysics scientists and other networks, hierarchical clustering method and the algorithm of Girvan and Newman are applied to detect their community structure. The interesting results for community structure of econophysicists collaboration network are shown. A dissimilarity function D is proposed to quantitatively measure the difference between community structures obtained by different methods. Using this measurement, the differences between the process and community results obtained by aforementioned algorithms are given. The effectiveness of hierarchical clustering method and GN algorithm for detecting community structure in various networks is discussed.

Suggested Citation

  • Zhang, Peng & Li, Menghui & Wu, Jinshan & Di, Zengru & Fan, Ying, 2006. "The analysis and dissimilarity comparison of community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 367(C), pages 577-585.
    • Handle: RePEc:eee:phsmap:v:367:y:2006:i:c:p:577-585
    DOI: 10.1016/j.physa.2005.11.018
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    Cited by:

    1. Li, Xiaojia & Li, Menghui & Hu, Yanqing & Di, Zengru & Fan, Ying, 2010. "Detecting community structure from coherent oscillation of excitable systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(1), pages 164-170.
    2. Nie, Tingyuan & Fan, Bo & Wang, Zhenhao, 2022. "Complexity and robustness of weighted circuit network of placement," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).

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