Computer Science ›› 2022, Vol. 49 ›› Issue (1): 133-139.doi: 10.11896/jsjkx.201000179

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

Generative Adversarial Network and Meta-path Based Heterogeneous Network Representation Learning

JIANG Zong-li, FAN Ke, ZHANG Jin-li   

  1. Department of Information Technology,Beijing University of Technology,Beijing 100124,China
  • Received:2020-10-29 Revised:2021-03-17 Online:2022-01-15 Published:2022-01-18
  • About author:JIANG Zong-li,born in 1956,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include network information search and processing.
    FAN Ke,born in 1996,postgraduate.His main research interests include he-terogeneous information network representation learning and so on.

Abstract: Most of the information works in real world are heterogeneous information networks (HIN).Network representation methods aiming to represent node data in low dimensional space have been widely used to analyze heterogeneous information networks,so as to effectively integrate rich semantic information and structural information in heterogeneous networks.However,the existing heterogeneous networks representation methods usually use negative sampling to select nodes randomly from the network,and the heterogeneity learning ability of nodes and edges is insufficient.Inspired by the generative adversarial networks (GAN) and meta-path,we propose a new framework,which is improved by weighted meta-path based sampling strategy.The samples can better reflect the direct and indirect relationship between nodes and enhance the semantic association of samples.In the process of generation and confrontation,the model fully considers the heterogeneity of nodes and edges,and has the ability of relationship perception,so as to realize the representation learning of heterogeneous information networks.The experimental results show that,compared with the current representation algorithms,the representation vectors learned by the model have better performance in classification and link prediction experiments.

Key words: Deep lear-ning, Generative adversarial network, Heterogeneous information networks, Meta-path, Network representation learning

CLC Number: 

  • TP183
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