• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2013, Vol. 35 ›› Issue (12): 76-83.

• 论文 • 上一篇    下一篇

复杂网络中重叠社区检测

张振宇,张珍,杨文忠,吴晓红   

  1. (新疆大学信息科学与工程学院,新疆 乌鲁木齐 830046)
  • 收稿日期:2013-08-02 修回日期:2013-10-22 出版日期:2013-12-25 发布日期:2013-12-25
  • 基金资助:

    国家自然科学基金资助项目(61262089,61262087)

Detecting overlapping communities in complex networks    

ZHANG Zhenyu,ZHANG Zhen,YANG Wenzhong,WU Xiaohong   

  1. (School of Information Science and Engineering,Xinjiang University,Urumqi 830046,China)
  • Received:2013-08-02 Revised:2013-10-22 Online:2013-12-25 Published:2013-12-25

摘要:

社区检测是研究复杂网络结构的基础。在分析现有重叠社区检测算法的基础上,提出了一种基于边

的重叠社区发现算法SAEC。算法将社区看成是由边构成的集合,通过定义边的相似度,得到概率转移矩阵

。利用谱聚类方法自动确定社区数目,最后调用Kmeans算法实现重叠社区划分。通过随机生成网络和真

实网络的测试,验证了该算法的有效性。

关键词: 网络社区, 谱聚类, 边, 检测

Abstract:

Community detection is the key problem in studying complex network structure. An

overlapping community detection algorithm SAEC based on edges is proposed based on the

analysis of those existing means. The SAEC algorithm regards the community as a set of

edges, defines the edge similarity and obtains the transition probability matrix. Using

spectral clustering method, the number of communities is automatically determined by

calculating the eigenvalues and eigenvectors of the transition probability matrix. Then, the

overlapping community detection is completed by Kmeans algorithm. The effectiveness of the

algorithm is verified by randomly generated test networks and real networks.

Key words: network community;spectral cluster;edge;detection