王立敏, 高学东, 武森. 基于最小社团链接度增量的社团结构挖掘算法[J]. 工程科学学报, 2009, 31(1): 112-117. DOI: 10.13374/j.issn1001-053x.2009.01.013
引用本文: 王立敏, 高学东, 武森. 基于最小社团链接度增量的社团结构挖掘算法[J]. 工程科学学报, 2009, 31(1): 112-117. DOI: 10.13374/j.issn1001-053x.2009.01.013
WANG Li-min, GAO Xue-dong, WU Sen. Mining algorithm of community structure based on the minimal increment of link degree of a community[J]. Chinese Journal of Engineering, 2009, 31(1): 112-117. DOI: 10.13374/j.issn1001-053x.2009.01.013
Citation: WANG Li-min, GAO Xue-dong, WU Sen. Mining algorithm of community structure based on the minimal increment of link degree of a community[J]. Chinese Journal of Engineering, 2009, 31(1): 112-117. DOI: 10.13374/j.issn1001-053x.2009.01.013

基于最小社团链接度增量的社团结构挖掘算法

Mining algorithm of community structure based on the minimal increment of link degree of a community

  • 摘要: 针对复杂网络社团结构挖掘算法复杂度高的问题,定义了一个衡量局部社团结构的指标,提出了一种基于最小社团链接度增量的社团结构挖掘算法.本算法的时间复杂度为O(kd),其中d为网络的平均节点度数,k为搜索的节点数.为了验证本算法的性能和计算的准确性,把本算法与一种经典的挖掘局部社团结构方法——Clauset算法,进行了比较.实验结果表明:本算法抽取的社团结构与Clauset算法相比基本一致,但在性能上有了显著提高.

     

    Abstract: A measure of local community structure was defined, and an mining algorithm of local community structure based on the minimal increment of link degree of a community was presented for resolving the time complexity problems of finding local community structure in complex networks. The algorithm ran in time O (kd) for general graphs, where d is the mean degree and k is the number of vertices to be explored. In order to determine its performance and calculation precision, the algorithm was compared with the classical local community identification approach, Clauset algorithm. Experimental results show that mining results of the algorithm are as effective as those of Clauset algorithm on the whole, and the algorithm is much faster than Clauset algorithm.

     

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