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Community detection in dynamic networks via adaptive label propagation

Author

Listed:
  • Jihui Han
  • Wei Li
  • Longfeng Zhao
  • Zhu Su
  • Yijiang Zou
  • Weibing Deng

Abstract

An adaptive label propagation algorithm (ALPA) is proposed to detect and monitor communities in dynamic networks. Unlike the traditional methods by re-computing the whole community decomposition after each modification of the network, ALPA takes into account the information of historical communities and updates its solution according to the network modifications via a local label propagation process, which generally affects only a small portion of the network. This makes it respond to network changes at low computational cost. The effectiveness of ALPA has been tested on both synthetic and real-world networks, which shows that it can successfully identify and track dynamic communities. Moreover, ALPA could detect communities with high quality and accuracy compared to other methods. Therefore, being low-complexity and parameter-free, ALPA is a scalable and promising solution for some real-world applications of community detection in dynamic networks.

Suggested Citation

  • Jihui Han & Wei Li & Longfeng Zhao & Zhu Su & Yijiang Zou & Weibing Deng, 2017. "Community detection in dynamic networks via adaptive label propagation," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-16, November.
    • Handle: RePEc:plo:pone00:0188655
    DOI: 10.1371/journal.pone.0188655
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    References listed on IDEAS

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    2. Steven H. Strogatz, 2001. "Exploring complex networks," Nature, Nature, vol. 410(6825), pages 268-276, March.
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    Cited by:

    1. Longfeng Zhao & Chao Wang & Gang-Jin Wang & H. Eugene Stanley & Lin Chen, 2021. "Community detection and portfolio optimization," Papers 2112.13383, arXiv.org.
    2. Zhao, Longfeng & Wang, Gang-Jin & Wang, Mingang & Bao, Weiqi & Li, Wei & Stanley, H. Eugene, 2018. "Stock market as temporal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 1104-1112.

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