Adaptive graph contrastive learning with joint optimization of data augmentation and graph encoder-Reference-Cited by-同舟云学术

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Adaptive graph contrastive learning with joint optimization of data augmentation and graph encoder

Published:2023-10-12 Issue: Volume: Page:
ISSN:0219-1377
Container-title:Knowledge and Information Systems
language:en
Short-container-title:Knowl Inf Syst

Author:

Wu Zhenpeng,Chen Jiamin,Al-Sabri Raeed,Oloulade Babatounde Moctard,Gao Jianliang

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Hardware and Architecture,Human-Computer Interaction,Information Systems,Software

Link

https://link.springer.com/content/pdf/10.1007/s10115-023-01979-3.pdf

Reference45 articles.

1. Tao Zhang, Tianqing Zhu, Mengde Han, Fengwen Chen, Jing Li, Zhou Wanlei Yu, Philip S (2023) Fairness in graph-based semi-supervised learning. Knowl Inf Syst 65:543–570

2. Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprintarXiv:1609.02907

3. Sankar Aravind, Wang Junting, Krishnan Adit, Sundaram Hari (2022) Self-supervised role learning for graph neural networks. Knowl Inf Syst 64:2091–2121

4. Li Jingci, Guangquan Lu, Zhengtian Wu, Ling Fuqing (2023) Multi-view representation model based on graph autoencoder. Inf Sci 632:439–453

5. Guangquan Lu, Huang Jihong (2021) Learning representation from concurrence-words graph for aspect sentiment classification. Comput J 64(7):1069–1079

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