Learning on Graphs under Label Noise
Author:
Affiliation:
1. Peking University,National Key Laboratory for Multimedia Information Processing,Beijing,China
2. University of California,Department of Computer Science,Los Angeles,USA
Funder
Research and Development
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10094559/10094560/10096088.pdf?arnumber=10096088
Reference27 articles.
1. Infogcl: Information-aware graph contrastive learning;xu;NeurIPS,2021
2. Graph Contrastive Learning with Adaptive Augmentation
3. Graph contrastive learning with augmentations;you;NeurIPS,2020
4. Negative sampling strategies for contrastive self-supervised learning of graph representations
5. Learning with instance-dependent label noise: A sample sieve approach;cheng,2020
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2. Resurrecting Label Propagation for Graphs with Heterophily and Label Noise;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24
3. Label noise correction for crowdsourcing using dynamic resampling;Engineering Applications of Artificial Intelligence;2024-07
4. Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts;IEEE Transactions on Big Data;2023-12
5. ALEX: Towards Effective Graph Transfer Learning with Noisy Labels;Proceedings of the 31st ACM International Conference on Multimedia;2023-10-26
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