| Deciphering spatio-temporal graph forecasting: A causal lens and treatment Y Xia, Y Liang, H Wen, X Liu, K Wang, Z Zhou, R Zimmermann Conference on Neural Information Processing Systems (Neurips 2023), 2023 | 48 | 2023 |
| Practical synthetic human trajectories generation based on variational point processes Q Long, H Wang, T Li, L Huang, K Wang, Q Wu, G Li, Y Liang, L Yu, Y Li Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 37 | 2023 |
| Searching Lottery Tickets in Graph Neural Networks: A Dual Perspective K Wang, Y Liang, P Wang, X Wang, P Gu, J Fang, Y Wang The Eleventh International Conference on Learning Representations (ICLR 2023), 2022 | 37 | 2022 |
| Beyond llava-hd: Diving into high-resolution large multimodal models YF Zhang, Q Wen, C Fu, X Wang, Z Zhang, L Wang, R Jin arXiv preprint arXiv:2406.08487, 2024 | 34 | 2024 |
| Exgc: Bridging efficiency and explainability in graph condensation J Fang, X Li, Y Sui, Y Gao, G Zhang, K Wang, X Wang, X He (corresponding author) WWW 2024, 2024 | 30 | 2024 |
| Brave the Wind and the Waves: Discovering Robust and Generalizable Graph Lottery Tickets K Wang, Y Liang, X Li, G Li, B Ghanem, R Zimmermann, H Yi, Y Zhang, ... IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI 2023), 2023 | 30 | 2023 |
| Maintaining the Status Quo: Capturing Invariant Relations for OOD Spatiotemporal Learning Z Zhou, Q Huang, K Yang, K Wang, X Wang, Y Zhang, Y Liang, Y Wang KDD 2023, 2023 | 30 | 2023 |
| Earthfarseer: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model H Wu, S Wang, Y Liang, Z Zhou, W Huang, W Xiong, K Wang (* corresponding author) AAAI 2024, 2024 | 29 | 2024 |
| MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans? YF Zhang, H Zhang, H Tian, C Fu, S Zhang, J Wu, F Li, K Wang, Q Wen, ... arXiv preprint arXiv:2408.13257 (benchmark research), 2024 | 24 | 2024 |
| The Snowflake Hypothesis: Training and Powering GNN with One Node One Receptive Field K Wang, G Li, S Wang, G Zhang, K Wang, Y You, J Fang, X Peng, Y Liang, ... KDD 2024, 3152-3163, 2024 | 22* | 2024 |
| Moltc: Towards molecular relational modeling in language models J Fang, S Zhang, C Wu, Z Yang, Z Liu, S Li, K Wang, W Du, X Wang arXiv preprint arXiv:2402.03781, 2024 | 20 | 2024 |
| Graph Lottery Ticket Automated G Zhang, K Wang, W Huang, Y Yue, Y Wang, R Zimmermann, A Zhou, ... ICLR 2024 (Equal Contribution), 2023 | 15 | 2023 |
| Two heads are better than one: Boosting graph sparse training via semantic and topological awareness G Zhang, Y Yue, K Wang, J Fang, Y Sui, K Wang, Y Liang, D Cheng, ... arXiv preprint arXiv:2402.01242 (ICML 2024 Corresponding author), 2024 | 14 | 2024 |
| Urbanvlp: A multi-granularity vision-language pre-trained foundation model for urban indicator prediction X Hao, W Chen, Y Yan, S Zhong, K Wang, Q Wen, Y Liang arXiv e-prints, arXiv: 2403.16831, 2024 | 13 | 2024 |
| Modeling spatio-temporal dynamical systems with neural discrete learning and levels-of-experts K Wang, H Wu, G Zhang, J Fang, Y Liang, Y Wu, R Zimmermann, Y Wang IEEE Transactions on Knowledge and Data Engineering 36 (8), 4050-4062, 2024 | 13 | 2024 |
| Graph neural networks in EEG-based emotion recognition: a survey C Liu, X Zhou, Y Wu, R Yang, Z Wang, L Zhai, Z Jia, Y Liu arXiv preprint arXiv:2402.01138, 2024 | 13 | 2024 |
| CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks Y Duan, G Zhang, S Wang, X Peng, W Ziqi, J Mao, H Wu, X Jiang, K Wang (Corresponding author *) arXiv preprint arXiv:2402.14708, 2024 | 11 | 2024 |
| Attend who is weak: Enhancing graph condensation via cross-free adversarial training X Li, K Wang, H Deng, Y Liang, D Wu (* Corresponding author) arXiv preprint arXiv:2311.15772, 2023 | 11 | 2023 |
| A2DJP: A two graph-based component fused learning framework for urban anomaly distribution and duration joint-prediction K Wang, Z Zhou, X Wang, P Wang, Q Fang, Y Wang IEEE Transactions on Knowledge and Data Engineering (TKDE 2022), 2022 | 11 | 2022 |
| Explainable and interpretable multimodal large language models: A comprehensive survey Y Dang, K Huang, J Huo, Y Yan, S Huang, D Liu, M Gao, J Zhang, C Qian, ... arXiv preprint arXiv:2412.02104, 2024 | 10 | 2024 |
Yuxuan LiangAssistant Professor, Hong Kong University of Science and Technology (Guangzhou)Patvirtintas el. paštas hkust-gz.edu.cn