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A novel dynamics combination model reveals the hidden information of community structure

Author

Listed:
  • Hui-Jia Li

    (School of Management Science and Engineering, Central University of Finance and Economics, Beijing 100080, P. R. China;
    Academy of Mathematics and Systems Science, Chinese Academy of Science, Beijing 100190, P. R. China)

  • Huiying Li

    (Department of Automation, Tsinghua University, Beijing 100084, P. R. China)

  • Chuanliang Jia

    (School of Management Science and Engineering, Central University of Finance and Economics, Beijing 100080, P. R. China)

Abstract

The analysis of the dynamic details of community structure is an important question for scientists from many fields. In this paper, we propose a novel Markov–Potts framework to uncover the optimal community structures and their stabilities across multiple timescales. Specifically, we model the Potts dynamics to detect community structure by a Markov process, which has a clear mathematical explanation. Then the local uniform behavior of spin values revealed by our model is shown that can naturally reveal the stability of hierarchical community structure across multiple timescales. To prove the validity, phase transition of stochastic dynamic system is used to indicate that the stability of community structure we proposed is able to describe the significance of community structure based on eigengap theory. Finally, we test our framework on some example networks and find it does not have resolute limitation problem at all. Results have shown the model we proposed is able to uncover hierarchical structure in different scales effectively and efficiently.

Suggested Citation

  • Hui-Jia Li & Huiying Li & Chuanliang Jia, 2015. "A novel dynamics combination model reveals the hidden information of community structure," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 26(04), pages 1-13.
    • Handle: RePEc:wsi:ijmpcx:v:26:y:2015:i:04:n:s0129183115500436
    DOI: 10.1142/S0129183115500436
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    Cited by:

    1. Fei, Liguo & Deng, Yong, 2017. "A new method to identify influential nodes based on relative entropy," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 257-267.

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