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The adaptive dynamic community detection algorithm based on the non-homogeneous random walking

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  • Xin, Yu
  • Xie, Zhi-Qiang
  • Yang, Jing

Abstract

With the changing of the habit and custom, people’s social activity tends to be changeable. It is required to have a community evolution analyzing method to mine the dynamic information in social network. For that, we design the random walking possibility function and the topology gain function to calculate the global influence matrix of the nodes. By the analysis of the global influence matrix, the clustering directions of the nodes can be obtained, thus the NRW (Non-Homogeneous Random Walk) method for detecting the static overlapping communities can be established. We design the ANRW (Adaptive Non-Homogeneous Random Walk) method via adapting the nodes impacted by the dynamic events based on the NRW. The ANRW combines the local community detection with dynamic adaptive adjustment to decrease the computational cost for ANRW. Furthermore, the ANRW treats the node as the calculating unity, thus the running manner of the ANRW is suitable to the parallel computing, which could meet the requirement of large dataset mining. Finally, by the experiment analysis, the efficiency of ANRW on dynamic community detection is verified.

Suggested Citation

  • Xin, Yu & Xie, Zhi-Qiang & Yang, Jing, 2016. "The adaptive dynamic community detection algorithm based on the non-homogeneous random walking," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 241-252.
    • Handle: RePEc:eee:phsmap:v:450:y:2016:i:c:p:241-252
    DOI: 10.1016/j.physa.2016.01.025
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    References listed on IDEAS

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    1. Guo, Chonghui & Wang, Jiajia & Zhang, Zhen, 2014. "Evolutionary community structure discovery in dynamic weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 565-576.
    2. Zhou, Xu & Liu, Yanheng & Li, Bin & Sun, Geng, 2015. "Multiobjective biogeography based optimization algorithm with decomposition for community detection in dynamic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 430-442.
    3. Zhong, Weiqiong & An, Haizhong & Gao, Xiangyun & Sun, Xiaoqi, 2014. "The evolution of communities in the international oil trade network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 42-52.
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    Cited by:

    1. Ke Hu & Ju Xiang & Yun-Xia Yu & Liang Tang & Qin Xiang & Jian-Ming Li & Yong-Hong Tang & Yong-Jun Chen & Yan Zhang, 2020. "Significance-based multi-scale method for network community detection and its application in disease-gene prediction," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-24, March.
    2. 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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