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A label propagation approach based on local optimization

Author

Listed:
  • Xiaohong Zhang

    (College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, P. R. China)

  • Yulin Jiang

    (College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, P. R. China)

  • Jianji Ren

    (College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, P. R. China)

  • Chaosheng Tang

    (College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, P. R. China)

Abstract

Community detection offers an important way to understand the structures and functions of social network. The label propagation algorithm has attracted vast attention since it is very suitable for discovering communities from large-scale networks. However, the algorithm suffers from the instability and inefficiency problem caused by the random policies it adopted. In this paper, we propose a novel label propagation approach based on local optimization to deal with the problem. The approach introduces a pre-propagation mechanism to optimize randomly initialized labels according to special factors, for example, node compactness. After that, it traverses and relabels nodes in the descending order of aggregate influence. The experiment results demonstrate the usefulness and effectiveness of our approach.

Suggested Citation

  • Xiaohong Zhang & Yulin Jiang & Jianji Ren & Chaosheng Tang, 2018. "A label propagation approach based on local optimization," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 29(06), pages 1-16, June.
    • Handle: RePEc:wsi:ijmpcx:v:29:y:2018:i:06:n:s012918311850047x
    DOI: 10.1142/S012918311850047X
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    Cited by:

    1. Wang, Yan & Cao, Xinxin & Weng, Tongfeng & Yang, Huijie & Gu, Changgui, 2021. "A convex principle of search time for a multi-biased random walk on complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).

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