Semi-supervised co-clustering on attributed heterogeneous information networks

• Main Authors: JI, Yugang, SHI, Chuan, FANG, Yuan, KONG, Xiangnan, YIN, Mingyang
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: co-clustering; heterogeneous information network; meta-paths; matrix tri-factorization; semi-supervised learning; Databases and Information Systems; OS and Networks
• Online Access: https://ink.library.smu.edu.sg/sis_research/5291; https://ink.library.smu.edu.sg/context/sis_research/article/6294/viewcontent/IPM20_SCCAIN.pdf
• Institution: Singapore Management University

Node clustering on heterogeneous information networks (HINs) plays an important role in many real-world applications. While previous research mainly clusters same-type nodes independently via exploiting structural similarity search, they ignore the correlations of different-type nodes. In this paper...