Heterogeneous Information Networks Bi-clustering with Similarity Regularization

  • Xianchao Zhang
  • Haixin Li
  • Wenxin LiangEmail author
  • Linlin Zong
  • Xinyue Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9650)


Clustering analysis of multi-typed objects in heterogeneous information network (HINs) is an important and challenging problem. Nonnegative Matrix Tri-Factorization (NMTF) is a popular bi-clustering algorithm on document data and relational data. However, few algorithms utilize this method for clustering in HINs. In this paper, we propose a novel bi-clustering algorithm, BMFClus, for HIN based on NMTF. BMFClus not only simultaneously generates clusters for two types of objects but also takes rich heterogeneous information into account by using a similarity regularization. Experiments on both synthetic and real-world datasets demonstrate that BMFClus outperforms the state-of-the-art methods.


Heterogeneous information network Nonnegative Matrix Tri-Factorization Clustering 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Xianchao Zhang
    • 1
  • Haixin Li
    • 1
  • Wenxin Liang
    • 1
    Email author
  • Linlin Zong
    • 1
  • Xinyue Liu
    • 1
  1. 1.Dalian University of TechnologyDalianChina

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