Semi-supervised Learning on Graphs Using Adversarial Training with Generated Sample

Semi-supervised Learning on Graphs Using Adversarial Training with Generated Sample

Given a graph composed of a small number of labeled nodes and a large number of unlabeled nodes, semi-supervised learning on graphs aims to assign labels for the unlabeled nodes. Generative adversarial networks have shown strong ability in semi-supervised learning, but the research of generative adv...

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Bibliographic Details
Main Author: WANG Cong, WANG Jie, LIU Quanming, LIANG Jiye
Format: Article
Language:zho
Published: Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 2023-02-01
Series:Jisuanji kexue yu tansuo
Subjects:
semi-supervised learning on graphs; generative adversarial networks (gan); adversarial training; ge-nerated sample; graph embedding
Online Access:http://fcst.ceaj.org/fileup/1673-9418/PDF/2104119.pdf

Internet

http://fcst.ceaj.org/fileup/1673-9418/PDF/2104119.pdf

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