基于矩阵分解的二分网络社区挖掘算法  

Detecting Community Structure in Bipartite Networks Based on Matrix Factorization

作　　者：陈伯伦[1] 陈崚[2,3] 邹盛荣[2] 徐秀莲[4] 

机构地区：[1]南京航空航天大学计算机科学与技术学院,南京210016 [2]扬州大学信息学院计算机系,扬州225009 [3]南京大学软件新技术国家重点实验室,南京210093 [4]扬州大学物理科学与技术学院,扬州225009

出　　处：《计算机科学》2014年第2期55-58,101,共5页Computer Science

基　　金：国家自然科学基金项目(61070047;61070133;61003180);国家重点基础研究发展规划(973)项目(2012CB316003);江苏省自然科学基金项目(BK21010134);江苏省研究生创新基金(CXZZ13_0172)资助

摘　　要：二分网络社区挖掘对复杂网络有重要的理论意义和应用价值。提出了一个基于矩阵分解的二分网络社区挖掘算法。该算法首先将二分网络分为两个部分,每个部分尽可能保存完整的社区信息,然后分别对两个部分进行递归的拆分,直至不能拆分为止。在拆分的过程中,应用矩阵分解,使得到的分解能与网络的相关矩阵的行空间尽可能接近,即尽可能保持原图的社区信息。实验结果表明,该算法在不需任何额外参数的情况下,不但能较准确地识别实际网络的社区个数,而且可以获得很好的划分效果。Community detection in bipartite network is very important in the reseach on the theory and applications of complex network analysis. An algorithm for detecting community structure in bipartite networks based on matrix fac- torization was presented. The algorithm first partitions the network into two parts, each of which can reserve the com- munity information as much as possible, and then the two parts are further recursively partitioned until they can not be partitioned. When partitioning the network, we used the approach of matrix decomposition so that the row space of the associated matrix of the networks can be approximated as close as possible and the community information can be re- served the as much as possible. Experimental results show that our algorithm can not only accurately identify the num- ber of communities of a network, but also obtain higher quality of community partitioning without previously known parameters.

关 键 词：二分网络 矩阵分解 社区检测 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]