Semi-supervised Co-Clustering on Attributed Heterogeneous Information Networks
作者：
Highlights：
• Designing a learnable overall relevance measure that makes full use of structural and attributed information by integrating HeteSim and attribute projection.
• Proposing a constrained negative matrix tri-factorization which utilizes pairwise constraints to cluster nodes of different types at the same time and give a closed solution.
• Modeling a unified framework to simultaneously co-cluster different-type nodes and mine the latent relevance between heterogeneous clusters on realworld attributed HINs such as e-commerce platforms and bibliographic networks.
摘要
•Designing a learnable overall relevance measure that makes full use of structural and attributed information by integrating HeteSim and attribute projection.•Proposing a constrained negative matrix tri-factorization which utilizes pairwise constraints to cluster nodes of different types at the same time and give a closed solution.•Modeling a unified framework to simultaneously co-cluster different-type nodes and mine the latent relevance between heterogeneous clusters on realworld attributed HINs such as e-commerce platforms and bibliographic networks.
论文关键词：Co-clustering,Heterogeneous information network,Meta-paths,Matrix tri-factorization,Semi-supervised learning
论文评审过程：Received 30 December 2019, Revised 7 May 2020, Accepted 9 June 2020, Available online 17 July 2020, Version of Record 17 July 2020.
论文官网地址：https://doi.org/10.1016/j.ipm.2020.102338
