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2025 Conference Publication Contrastive graph condensation: advancing data versatility through self-supervised learningGao, Xinyi, Li, Yayong, Chen, Tong, Ye, Guanhua, Zhang, Wentao and Yin, Hongzhi (2025). Contrastive graph condensation: advancing data versatility through self-supervised learning. The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Toronto, Canada, 3-7 August 2025. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3711896.3736892 |
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2025 Conference Publication Inductive graph few-shot class incremental learningLi, Yayong, Moghadam, Peyman, Peng, Can, Ye, Nan and Koniusz, Piotr (2025). Inductive graph few-shot class incremental learning. 18th International Conference on Web Search and Data Mining-WSDM, Hannover, Germany, 10-14 March 2025. New York, NY, United States: ACM. doi: 10.1145/3701551.3703578 |
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2024 Conference Publication Graph condensation for open-world graph learningGao, Xinyi, Chen, Tong, Zhang, Wentao, Li, Yayong, Sun, Xiangguo and Yin, Hongzhi (2024). Graph condensation for open-world graph learning. KDD '24: 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Barcelona, Spain, 25-29 August 2024. New York, NY, United States: ACM. doi: 10.1145/3637528.3671917 |
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2022 Conference Publication Towards deepening graph neural networks: a GNTK-based optimization perspectiveHuang, Wei, Li, Yayong, Du, Weitao, Yin, Jie, Xu, Richard Yi Da, Chen, Ling and Zhang, Miao (2022). Towards deepening graph neural networks: a GNTK-based optimization perspective. International Conference on Learning Representations 2022, Virtual, 25-29 April 2022. Appleton, WI USA: International Conference on Learning Representations. doi: 10.48550/arXiv.2103.03113 |
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2021 Conference Publication Unified robust training for graph neural networks against label noiseLi, Yayong, Yin, Jie and Chen, Ling (2021). Unified robust training for graph neural networks against label noise. 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Virtual, 11-14 May 2021. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-75762-5_42 |
