东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2008, Vol. 29 ›› Issue (7): 944-947.DOI: -

• 论著 • 上一篇    下一篇

基于多维特征向量的网络社团划分方法

葛新;赵海;张昕;李超;   

  1. 东北大学信息科学与工程学院;东北大学信息科学与工程学院;东北大学信息科学与工程学院;东北大学信息科学与工程学院 辽宁沈阳110004;辽宁沈阳110004;辽宁沈阳110004;辽宁沈阳110004
  • 收稿日期:2013-06-22 修回日期:2013-06-22 出版日期:2008-07-15 发布日期:2013-06-22
  • 通讯作者: Ge, X.
  • 作者简介:-
  • 基金资助:
    国家高技术产业化示范工程项目(20012167)

Division based on multidimensional eigenvector for web communities

Ge, Xin (1); Zhao, Hai (1); Zhang, Xin (1); Li, Chao (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110004, China
  • Received:2013-06-22 Revised:2013-06-22 Online:2008-07-15 Published:2013-06-22
  • Contact: Ge, X.
  • About author:-
  • Supported by:
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摘要： 为了寻找大规模复杂网络中的社团结构,提出了基于多维特征向量的社团划分方法,即多维特征向量谱平分法.利用网络连接矩阵的多维特征向量划分网络社团,通过仿真实验分析关键参数对划分效果的影响,从而确定使得划分结果最优的参量值,并综合多维特征量阈值和社团数目两方面的因素决定被划分的社团数目.在具有代表性的局域世界网络演化模型中进行仿真,证明该方法在网络聚簇特征不是很明显的情况下,能够有效划分网络中存在的多个社团,适应具有各种聚集特征的网络,说明该算法在实际网络中具有较高的应用价值.

关键词: 复杂网络, 社团结构, 谱平分法, 多维特征向量, 聚类系数

Abstract: A spectral bisection algorithm based on multidimensional eigenvector is proposed for the division of web communities in large-scale complex networks, according to different community structures. It identifies those communities by way of the multidimensional eigenvectors of network's connection matrices, and then the effects of key parameters on the division results are analyzed through simulation so as to determine the parametrical values which make sure that the results of division are optimum. And the number of communities being identified can thus be confirmed by the factors integrating the threshold values of multidimensional eigenvector with the total number of communities in a network. The results of simulation done in a locally typical WWW evolution model demonstrated that the algorithm proposed enables the efficient division of many communities in relevant network where the clustering characteristics aren't so obvious. In addition, the algorithm is adaptable to the networks with various clustering characteristics, thus showing actually its high applicability to networks.

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