基于模块度增量的二分网络社区挖掘算法

Algorithm for Mining Bipartite Network Based on Incremental Modularity

针对二分网络的社区挖掘问题,提出了一种基于模块度增量的二分网络社区挖掘算法。该算法假设每个顶点独自构成一个社区,并具有自己的标号。其中,一部分顶点将自己的标号复制并传递到另一部分中的某个顶点上,使之与其位于同一个社区;另一部分的顶点实施同样的操作。如此反复迭代,直至收敛。标号传播时,选择模块度增量最大的边进行传送,使整体模块度不断提高。在真实数据集上进行的测试表明,所提算法能对二分网络进行高质量的社区划分。

Aiming at mining communities from bipartite network,an algorithm based on incremental modularity was proposed.The algorithm assumes that each vertex constitutes a community by itself with its own label.A part of the vertex copies its own label and passes it to a vertex on another part,so that it is located in the same community,and then it performs the same operation on the vertices of another part,and repeats iterations until convergence.In label propagation,the algorithm chooses the edge with the largest incremental modularity,so that the overall modularity is constantly improving.The experimental results on real datasets show that the proposed algorithm can mine high quality communities from bipartite network.