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基于节点相似性度量的社团结构划分方法
  • 摘要

    针对复杂网络结构划分过程复杂、准确性差的问题,定义了节点全局和局部相似性衡量指标,并构建节点的相似性矩阵,提出一种基于节点相似性度量的社团结构划分算法.其基本思路是将节点(或社团)按相似性合并条件划分到同一个社团中,如果合并后的节点(或社团)仍然满足相似性合并条件,则继续合并,直到所有节点都得到准确的社团划分.实验结果表明,所提算法能成功正确地划分出真实网络中的社团结构,性能比标签传播算法(LPA)、GN (Girvan-Newman)、CNM (Clauset-Newman-Moore)等算法优秀,能有效提高结果的准确性和鲁棒性.

  • 作者

    梁宗文  杨帆  李建平  LIANG Zongwen  YANG Fan  LI Jianping 

  • 作者单位

    电子科技大学计算机科学与工程学院,成都611731;Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway 08854, USA/电子科技大学计算机科学与工程学院,成都,611731

  • 刊期

    2015年5期 ISTIC PKU

  • 关键词

    节点相似性  社团划分  社团结构  复杂网络  node similarity  community detection  community structure  complex network 

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