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Open Access

Nonnegative Matrix Tri-Factorization Based Clustering in a Heterogeneous Information Network with Star Network Schema

College of Computer Science and Technology, Jilin University, Changchun 130012, China
School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China
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Heterogeneous Information Networks (HINs) contain multiple types of nodes and edges; therefore, they can preserve the semantic information and structure information. Cluster analysis using an HIN has obvious advantages over a transformation into a homogenous information network, which can promote the clustering results of different types of nodes. In our study, we applied a Nonnegative Matrix Tri-Factorization (NMTF) in a cluster analysis of multiple metapaths in HIN. Unlike the parameter estimation method of the probability distribution in previous studies, NMTF can obtain several dependent latent variables simultaneously, and each latent variable in NMTF is associated with the cluster of the corresponding node in the HIN. The method is suited to co-clustering leveraging multiple metapaths in HIN, because NMTF is employed for multiple nonnegative matrix factorizations simultaneously in our study. Experimental results on the real dataset show that the validity and correctness of our method, and the clustering result are better than that of the existing similar clustering algorithm.


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Tsinghua Science and Technology
Pages 386-395
Cite this article:
Hu J, Xing Y, Han M, et al. Nonnegative Matrix Tri-Factorization Based Clustering in a Heterogeneous Information Network with Star Network Schema. Tsinghua Science and Technology, 2022, 27(2): 386-395.








Web of Science






Received: 20 September 2020
Accepted: 09 October 2020
Published: 29 September 2021
© The author(s) 2022

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