Scientific Paper Heterogeneous Graph Node Representation Learning Method Based onUnsupervised Clustering Level

• Published in: Ji suan ji ke xue Vol. 49; no. 9; pp. 64 - 69
• Main Author: SONG Jie, LIANG Mei-yu, XUE Zhe, DU Jun-ping, KOU Fei-fei
• Format: Journal Article
• Language: Chinese
• Published: Editorial office of Computer Science 01.09.2022
• Subjects: scientific paper|heterogeneous graph network|graph representation learning|link prediction|unsupervised learning
• ISSN: 1002-137X
• DOI: 10.11896/jsjkx.220500196

Knowledge representation of scientific paper data is a problem to be solved,and how to learn the representation of paper nodes in scientific paper heterogeneous network is the core to solve this problem.This paper proposes an unsupervised cluster-level scientific paper heterogeneous graph node representation learning method(UCHL),aiming at obtaining the representation of nodes (authors,institutions,papers,etc.) in the heterogeneous graph of scientific papers.Based on the heterogeneous graph representation,this paper performs link prediction on the entire heterogeneous graph and obtains the relationship between the edges of the nodes,that is,the relationship between paper and paper.Experiments results show that the proposed method achieves excellent performance on multiple evaluation metrics on real scientific paper datasets.