Journal
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
Volume 515, Issue -, Pages 376-387Publisher
ELSEVIER
DOI: 10.1016/j.physa.2018.09.093
Keywords
Graph regularized; Geometrical structure; Nonnegative matrix tri-factorization; Community detection
Categories
Funding
- National Natural Science Foundation of China [61272109, 61502350]
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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. (C) 2018 Elsevier B.V. All rights reserved.
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