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DOI码：10.1016/j.physa.2018.09.093

所属单位：(1) School of Computer Science, Wuhan University, Wuhan; 430072, China

发表刊物：Physica A: Statistical Mechanics and its Applications

摘要：Non-negative Matrix Factorization technique has attracted many interests in overlapping community detection due to its performance and interpretability. However, when adapted to discover community structure the intrinsic geometric information of the network graph is seldom considered. In view of this, we proposed a novel NMF based algorithm called Graph regularized nonnegative matrix tri-factorization (GNMTF) model, which incorporates the intrinsic geometrical properties of the network graph by manifold regularization. Moreover, by using three factor matrices we can not only explicitly obtain the community membership of each node but also learn the interaction among different communities. The experimental results on two well-known real world networks and a benchmark network demonstrate the effectiveness of the algorithm over the representative non-negative matrix factorization based method. © 2018 Elsevier B.V.

合写作者： Wei(1), Yu, Hong(1), ShiJun(1), Li,Jin

是否译文：否

发表时间：2019-01-01