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

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.