Research on Heterogeneous Information Network Link Prediction Based on Representation Learning  

作　　者：Yan Zhao Weifeng Rao Zihui Hu Qi Zheng 

机构地区：[1]School of Information Engineering,Jingdezhen Ceramic University,Jingdezhen 333403,China [2]School of Mechanical and Electronic Engineering,Jingdezhen Ceramic University,Jingdezhen 333403,China

出　　处：《Journal of Electronic Research and Application》2024年第5期32-37,共6页电子研究与应用

基　　金：Science and Technology Research Project of Jiangxi Provincial Department of Education(Project No.GJJ211348,GJJ211347 and GJJ2201056)。

摘　　要：A heterogeneous information network,which is composed of various types of nodes and edges,has a complex structure and rich information content,and is widely used in social networks,academic networks,e-commerce,and other fields.Link prediction,as a key task to reveal the unobserved relationships in the network,is of great significance in heterogeneous information networks.This paper reviews the application of presentation-based learning methods in link prediction of heterogeneous information networks.This paper introduces the basic concepts of heterogeneous information networks,and the theoretical basis of representation learning,and discusses the specific application of the deep learning model in node embedding learning and link prediction in detail.The effectiveness and superiority of these methods on multiple real data sets are demonstrated by experimental verification.

关 键 词：Heterogeneous information network Link prediction Presentation learning Deep learning Node embedding 

分 类 号：TN9[电子电信—信息与通信工程]