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LPA-CBD an improved label propagation algorithm based on community belonging degree for community detection

Author

Listed:
  • Chun Gui

    (School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China)

  • Ruisheng Zhang

    (School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China)

  • Zhili Zhao

    (School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China)

  • Jiaxuan Wei

    (School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China)

  • Rongjing Hu

    (School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China)

Abstract

In order to deal with stochasticity in center node selection and instability in community detection of label propagation algorithm, this paper proposes an improved label propagation algorithm named label propagation algorithm based on community belonging degree (LPA-CBD) that employs community belonging degree to determine the number and the center of community. The general process of LPA-CBD is that the initial community is identified by the nodes with the maximum degree, and then it is optimized or expanded by community belonging degree. After getting the rough structure of network community, the remaining nodes are labeled by using label propagation algorithm. The experimental results on 10 real-world networks and three synthetic networks show that LPA-CBD achieves reasonable community number, better algorithm accuracy and higher modularity compared with other four prominent algorithms. Moreover, the proposed algorithm not only has lower algorithm complexity and higher community detection quality, but also improves the stability of the original label propagation algorithm.

Suggested Citation

  • Chun Gui & Ruisheng Zhang & Zhili Zhao & Jiaxuan Wei & Rongjing Hu, 2018. "LPA-CBD an improved label propagation algorithm based on community belonging degree for community detection," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 29(02), pages 1-13, February.
    • Handle: RePEc:wsi:ijmpcx:v:29:y:2018:i:02:n:s0129183118500110
    DOI: 10.1142/S0129183118500110
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    References listed on IDEAS

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    1. Liu, Dong & Liu, Xiao & Wang, Wenjun & Bai, Hongyu, 2014. "Semi-supervised community detection based on discrete potential theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 173-182.
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    Cited by:

    1. Hao Long & Xiao-Wei Liu, 2019. "A Unified Community Detection Algorithm In Large-Scale Complex Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 1-19, May.

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