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Detecting community structure from coherent oscillation of excitable systems

Author

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  • Li, Xiaojia
  • Li, Menghui
  • Hu, Yanqing
  • Di, Zengru
  • Fan, Ying

Abstract

Many networks are proved to have community structures. On the basis of the fact that the dynamics on networks are intensively affected by the related topology, in this paper the dynamics of excitable systems on networks and a corresponding approach for detecting communities are discussed. Dynamical networks are formed by interacting neurons; each neuron is described using the FHN model. For noisy disturbance and appropriate coupling strength, neurons may oscillate coherently and their behavior is tightly related to the community structure. Synchronization between nodes is measured in terms of a correlation coefficient based on long time series. The correlation coefficient matrix can be used to project network topology onto a vector space. Then by the K-means cluster method, the communities can be detected. Experiments demonstrate that our algorithm is effective at discovering community structure in artificial networks and real networks, especially for directed networks. The results also provide us with a deep understanding of the relationship of function and structure for dynamical networks.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:389:y:2010:i:1:p:164-170
    DOI: 10.1016/j.physa.2009.09.006
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    References listed on IDEAS

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    1. 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.
    2. Pablo M. Gleiser & Leon Danon, 2003. "Community Structure In Jazz," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 6(04), pages 565-573.
    3. Richard J. Williams & Neo D. Martinez, 2000. "Simple rules yield complex food webs," Nature, Nature, vol. 404(6774), pages 180-183, March.
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    Cited by:

    1. Bertrand M. Roehner, 2010. "Fifteen years of econophysics: worries, hopes and prospects," Papers 1004.3229, arXiv.org.

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