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Community Finding In Complex Networks

Author

Listed:
  • XIANGJUN SHEN

    (Department of Automation, University of Science and Technology of China, Hefei, Anhui, 230027, China;
    Department of Rado Engineering, Southeast University, Nanjing, Jiangsu, 210096, China)

  • ZENGFU WANG

    (Department of Automation, University of Science and Technology of China, Hefei, Anhui, 230027, China)

  • LENAN WU

    (Department of Automation, Department of Radio Engineering, Southeast University, Nanjing, Jiangsu, 210096, China)

Abstract

The investigation of community structures in complex networks is an important issue in many domains and disciplines. In this paper, we propose a novel method to address the problem based on evaluation of the community structure. By testing the proposed algorithm on artificial and real-world networks, experimental results demonstrate that our approach is both accurate and fast. Our algorithm may shed light on uncovering the universal principles of network architectures and topologies.

Suggested Citation

  • Xiangjun Shen & Zengfu Wang & Lenan Wu, 2006. "Community Finding In Complex Networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 17(07), pages 1055-1066.
  • Handle: RePEc:wsi:ijmpcx:v:17:y:2006:i:07:n:s012918310600976x
    DOI: 10.1142/S012918310600976X
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    References listed on IDEAS

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    Cited by:

    1. Xingyuan Wang & Xiaomeng Qin, 2017. "Detecting communities by asymmetric intimacy in directed-weighted network," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 28(01), pages 1-14, January.

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