37 / 2017-01-21 10:15:31
A Hybrid Biogeography-based Optimization and Differential Evolution Algorithm for Community Detection in Complex Networks
12587
终稿
李宇航 李 / 武汉理工大学
Community detection is a particularly important research subject in complex networks. In this paper, we use the hybrid Biogeography-based Optimization and Differential Evolution (BBO/DE) algorithm for community detection. In the previous work, we have proved that the BBD/DE algorithm has a better convergence ability of optimization. The BBD/DE algorithm does not need to know how many communities the network has. And the modularity function is the only criterion to evaluate community detection. We use the modularity function as the fitness function of the BBD/DE algorithm. As a result, the optimization algorithm can be combined with the community detection of complex network together well. As the performance measure, we choose both real network and computer generated network. Experimental results show that the BBO/DE algorithm can be used for community detection, and can achieve similar results with the traditional algorithms.
重要日期
  • 会议日期

    02月16日

    2017

    02月18日

    2017

  • 01月20日 2017

    初稿截稿日期

  • 01月30日 2017

    初稿录用通知日期

  • 02月10日 2017

    终稿截稿日期

  • 02月18日 2017

    注册截止日期

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