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2026 Journal Article Do digital twins need people? Integration of the human dimension into digital twins of the natural environmentDhakal, Sandeep, Parry, Hazel, Li, Yayong, Loechel, Barton and Moghadam, Peyman (2026). Do digital twins need people? Integration of the human dimension into digital twins of the natural environment. Socio-Environmental Systems Modelling, 8, 18760. doi: 10.18174/sesmo.18760 |
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2026 Conference Publication A margin enhanced data augmentation method for imbalanced credit default predictionZhang, Hong, Chen, Yuansheng, Liu, Ximing, Wu, Jinran, Liao, Yichao and Li, Yayong (2026). A margin enhanced data augmentation method for imbalanced credit default prediction. 2025 IEEE International Conference on Big Data (BigData), Macao, China, 8-11 December 2025. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/bigdata66926.2025.11401514 |
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2026 Journal Article A traffic flow forecasting model based on dynamic graph learning and temporally adaptive attentionZhang, Hong, Qi, Fangzheng, Zhang, Yu, Qin, Yijie and Li, Yayong (2026). A traffic flow forecasting model based on dynamic graph learning and temporally adaptive attention. Safety Science, 195 107063, 107063-195. doi: 10.1016/j.ssci.2025.107063 |
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2026 Journal Article AI ethics in geoscience: toward trustworthy and responsible innovationWu, 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, 7 (1) 100414, 1-4. doi: 10.1016/j.geosus.2026.100414 |
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2026 Conference Publication Generalized Few-Shot Node Classification via Training Set RefinementLi, Yayong, Zhang, Xubo, Zhang, Hong, Ye, Nan, Liu, Zongli and Wu, Jinran (2026). Generalized Few-Shot Node Classification via Training Set Refinement. 22nd Pacific Rim International Conference on Artificial Intelligence, PRICAI 2025, Wellington, New Zealand, 17-21 November 2025. Singapore: Springer. doi: 10.1007/978-981-95-7075-1_32 |
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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 Journal Article A hierarchical attention-based feature selection and fusion method for credit risk assessmentLiu, Ximing, Li, Yayong, Dai, Cheng and Zhang, Hong (2024). A hierarchical attention-based feature selection and fusion method for credit risk assessment. Future Generation Computer Systems, 160, 537-546. doi: 10.1016/j.future.2024.06.036 |
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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 Journal Article Informative pseudo-labeling for graph neural networks with few labelsLi, 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 |
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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 Journal Article SEAL: Semisupervised Adversarial Active Learning on attributed graphsLi, 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 |
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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 |
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2018 Journal Article An Efficient H.264/AVC to HEVC Transcoder for Real-Time Video Communication in Internet of VehiclesLiu, 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 |
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2017 Journal Article An adaptive CU size decision algorithm for HEVC intra prediction based on complexity classification using machine learningLiu, 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 |
