Dr Yayong Li
Postdoctoral Research Fellow
School of Mathematics and Physics
Researcher biography
Yayong Li has been a Postdoctoral Research Fellow under the supervision of Dr. Nan at The University of Queensland, where his research focuses on graph representation learning, PIML, and AI for agriculture. He specializes in machine-learning methodologies for dynamic and weakly supervised settings, such as incremental learning, few-shot learning, and learning under label noise.
Journal Articles
Wu, Jinran, Tian, Xin, Wang, You-Gan, Li, Tong, Liu, Qingyang, Li, Yayong, Cui, Lizhen, Tian, Zhuangcai, Xu, Jing, Lyu, Xianzhou and Mo, Yuming (2026). AI ethics in geoscience: toward trustworthy and responsible innovation. Geography and Sustainability 100414. doi: 10.1016/j.geosus.2026.100414
Li, Yayong, Yin, Jie and Chen, Ling (2022). Informative pseudo-labeling for graph neural networks with few labels. Data Mining and Knowledge Discovery, 37 (1), 228-254. doi: 10.1007/s10618-022-00879-4
Li, Yayong, Yin, Jie and Chen, Ling (2021). SEAL: Semisupervised Adversarial Active Learning on attributed graphs. IEEE Transactions on Neural Networks and Learning Systems, 32 (7) 9158558, 3136-3147. doi: 10.1109/tnnls.2020.3009682
Liu, Xingang, Li, Yayong, Dai, Cheng, Li, Pan and Yang, Laurence T. (2018). An Efficient H.264/AVC to HEVC Transcoder for Real-Time Video Communication in Internet of Vehicles. IEEE Internet of Things Journal, 5 (4), 3186-3197. doi: 10.1109/jiot.2018.2837034
Liu, Xingang, Li, Yayong, Liu, Deyuan, Wang, Peicheng and Yang, Laurence T. (2017). An adaptive CU size decision algorithm for HEVC intra prediction based on complexity classification using machine learning. IEEE Transactions on Circuits and Systems for Video Technology, 29 (1), 144-155. doi: 10.1109/tcsvt.2017.2777903
Conference Papers
Gao, 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
Li, 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
Gao, 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
Huang, 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
Li, 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