Computer Science ›› 2022, Vol. 49 ›› Issue (9): 64-69.doi: 10.11896/jsjkx.220500196

• Database & Big Data & Data Science • Previous Articles     Next Articles

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

SONG Jie, LIANG Mei-yu, XUE Zhe, DU Jun-ping, KOU Fei-fei   

  1. Beijing Key Laboratory of Intelligent Communication Software and Multimedia,School of Computer Science(National Pilot Software Engineering School),Beijing University of Posts and Telecommunications,Beijing 100876,China
  • Received:2022-05-20 Revised:2022-07-05 Online:2022-09-15 Published:2022-09-09
  • About author:SONG Jie,born in 1997,master.His main research interests include data mining,information retrieval and machine learning.
    LIANG Mei-yu,born in 1985,associate professor,Ph.D.Her main research interests include artificial intelligence,data mining,multimedia information processing and computer vision.
  • Supported by:
    National Key R & D Program of China(2018YFB1402600) and National Natural Science Foundation of China(61877006,61802028,62002027).

Abstract: 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.

Key words: Scientific paper, Heterogeneous graph network, Graph representation learning, Link prediction, Unsupervised learning

CLC Number: 

  • TP391
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