Semi-supervised domain adaptation on graphs with contrastive learning and minimax entropy-Reference-Cited by-同舟云学术

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Semi-supervised domain adaptation on graphs with contrastive learning and minimax entropy

Published:2024-05 Issue: Volume:580 Page:127469
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Xiao Jiaren^ORCID,Dai Quanyu,Shen Xiao^ORCID,Xie Xiaochen^ORCID,Dai Jing,Lam James,Kwok Ka-Wai^ORCID

Funder

Alexander von Humboldt Foundation

Innovation and Technology Commission

National Natural Science Foundation of China

Croucher Foundation

Research Grants Council, University Grants Committee

University Grants Committee

Publisher

Elsevier BV

Reference46 articles.

1. Representation learning on graphs: Methods and applications;Hamilton,2018

2. A comprehensive survey on graph neural networks;Wu;IEEE Trans. Neural Netw. Learn. Syst.,2021

3. Graph self-supervised learning: A survey;Liu;IEEE Trans. Knowl. Data Eng.,2022

4. A survey on network embedding;Cui;IEEE Trans. Knowl. Data Eng.,2019

5. Network together: Node classification via cross-network deep network embedding;Shen;IEEE Trans. Neural Netw. Learn. Syst.,2021

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