结构扩展的非负矩阵分解社区发现算法

提出结构扩展的非负矩阵分解社区发现算法(nonnegative matrix factorization with structure extension, NMF-SE),通过结构扩展,加强相邻节点结构相似性,提高节点间连接的稠密度,从而提高非负矩阵分解在社区发现中的表现。结构扩展过程使节点将自身结构以一定的比例传递给周围的节点,从而使相邻节点间能够得到对方的拓扑结构信息。该过程构造了新的特征矩阵,使非负矩阵分解(nonnegative matrix factorization, NMF)更好地适用于社区发现,在图正则化的半监督任务中能更好地融合先验信息。在人工网络和真实网络上进行试验验证的结果表明,NMF-SE算法有效提高了复杂网络社区发现的准确性。

Community detection using nonnegative matrix factorization with structure extension

Nonnegative matrix factorization with structure extension(NMF-SE)was proposed to enhance the structural similarity of adjacent nodes and increase the density of connections between nodes, which improved the performance of nonnegative matrix factorization in community detection. In the process of structure extension, nodes transmitted their own structure to the surrounding nodes in a certain proportion, so that adjacent nodes could get the topology information from each other. This process constructed a new feature matrix, which made nonnegative matrix factorization(NMF)more suitable for community detection. Meanwhile, in the semi-supervised task with graph regularization, the prior information could be better incorporated. The experimental results on synthetic network and real network showed that NMF-SE algorithm effectively improved the accuracy of community detection in complex network.