Negative as Positive: Enhancing Out-of-distribution Generalization for Graph Contrastive Learning-Reference-Cited by-同舟云学术

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Negative as Positive: Enhancing Out-of-distribution Generalization for Graph Contrastive Learning

Published:2024-07-10 Issue: Volume:35 Page:2548-2552
ISSN:
Container-title:Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Wang Zixu¹^ORCID,Xu Bingbing²^ORCID,Yuan Yige¹^ORCID,Shen Huawei²^ORCID,Cheng Xueqi²^ORCID

Affiliation:

1. CAS Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences; University of Chinese Academy of Sciences, Beijing, China

2. CAS Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626772.3657927

Reference37 articles.

1. Bridged-GNN: Knowledge Bridge Learning for Effective Knowledge Transfer

2. Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network

3. Causality and Independence Enhancement for Biased Node Classification

4. Mucong Ding Kezhi Kong Jiuhai Chen John Kirchenbauer Micah Goldblum David Wipf Furong Huang and Tom Goldstein. 2021. A closer look at distribution shifts and out-of-distribution generalization on graphs. (2021).

5. Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, and Jie Tang. 2020. Graph random neural networks for semi-supervised learning on graphs. Advances in neural information processing systems , Vol. 33 (2020), 22092--22103.

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