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Local community extraction in directed networks

Author

Listed:
  • Ning, Xuemei
  • Liu, Zhaoqi
  • Zhang, Shihua

Abstract

Network is a simple but powerful representation of real-world complex systems. Network community analysis has become an invaluable tool to explore and reveal the internal organization of nodes. However, only a few methods were directly designed for community-detection in directed networks. In this article, we introduce the concept of local community structure in directed networks and provide a generic criterion to describe a local community with two properties. We further propose a stochastic optimization algorithm to rapidly detect a local community, which allows for uncovering the directional modular characteristics in directed networks. Numerical results show that the proposed method can resolve detailed local communities with directional information and provide more structural characteristics of directed networks than previous methods.

Suggested Citation

  • Ning, Xuemei & Liu, Zhaoqi & Zhang, Shihua, 2016. "Local community extraction in directed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 258-265.
    • Handle: RePEc:eee:phsmap:v:452:y:2016:i:c:p:258-265
    DOI: 10.1016/j.physa.2016.01.101
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    Citations

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    Cited by:

    1. Hosseini-Pozveh, Maryam & Ghorbanian, Maedeh & Tabaiyan, Maryam, 2022. "A label propagation-based method for community detection in directed signed social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    2. Zhang, Weitong & Zhang, Rui & Shang, Ronghua & Li, Juanfei & Jiao, Licheng, 2019. "Application of natural computation inspired method in community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 130-150.
    3. Kerr, Sandy & Johnson, Kate & Weir, Stephanie, 2017. "Understanding community benefit payments from renewable energy development," Energy Policy, Elsevier, vol. 105(C), pages 202-211.

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