| Self-supervised learning on graphs: Contrastive, generative, or predictive L Wu, H Lin, C Tan, Z Gao, SZ Li IEEE Transactions on Knowledge and Data Engineering 35 (4), 4216-4235, 2021 | 227 | 2021 |
| Graphmixup: Improving class-imbalanced node classification on graphs by self-supervised context prediction L Wu, H Lin, Z Gao, C Tan, S Li arXiv preprint arXiv:2106.11133, 2021 | 61* | 2021 |
| Conditional Local Convolution for Spatio-temporal Meteorological Forecasting H Lin, Z Gao, Y Xu, L Wu, L Li, SZ Li Association for the Advancement of Artificial Intelligence 2022, 2022 | 45 | 2022 |
| Diffbp: Generative diffusion of 3d molecules for target protein binding H Lin, Y Huang, M Liu, X Li, S Ji, SZ Li arXiv preprint arXiv:2211.11214, 2022 | 43 | 2022 |
| MogaNet: Multi-order Gated Aggregation Network S Li, Z Wang, Z Liu, C Tan, H Lin, D Wu, Z Chen, J Zheng, SZ Li The Twelfth International Conference on Learning Representations, 2023 | 35* | 2023 |
| Knowledge distillation improves graph structure augmentation for graph neural networks L Wu, H Lin, Y Huang, SZ Li Advances in Neural Information Processing Systems 35, 11815-11827, 2022 | 35 | 2022 |
| Beyond homophily and homogeneity assumption: Relation-based frequency adaptive graph neural networks L Wu, H Lin, B Hu, C Tan, Z Gao, Z Liu, SZ Li IEEE Transactions on Neural Networks and Learning Systems, 2023 | 15 | 2023 |
| Exploring Generative Neural Temporal Point Process H Lin, L Wu, G Zhao, P Liu, SZ Li Transactions on Machine Learning Research, 2022 | 14 | 2022 |
| Extracting low-/high-frequency knowledge from graph neural networks and injecting it into MLPs: an effective GNN-to-MLP distillation framework L Wu, H Lin, Y Huang, T Fan, SZ Li Proceedings of the AAAI Conference on Artificial Intelligence 37 (9), 10351 …, 2023 | 12 | 2023 |
| Deep clustering and visualization for end-to-end high-dimensional data analysis L Wu, L Yuan, G Zhao, H Lin, SZ Li IEEE Transactions on Neural Networks and Learning Systems, 2022 | 12 | 2022 |
| Quantifying the knowledge in gnns for reliable distillation into mlps L Wu, H Lin, Y Huang, SZ Li International Conference on Machine Learning, 37571-37581, 2023 | 11 | 2023 |
| Gnn cleaner: Label cleaner for graph structured data J Xia, H Lin, Y Xu, C Tan, L Wu, S Li, SZ Li IEEE Transactions on Knowledge and Data Engineering, 2023 | 11* | 2023 |
| Homophily-enhanced self-supervision for graph structure learning: Insights and directions L Wu, H Lin, Z Liu, Z Liu, Y Huang, SZ Li IEEE Transactions on Neural Networks and Learning Systems, 2023 | 11 | 2023 |
| Multi-level disentanglement graph neural network L Wu, H Lin, J Xia, C Tan, SZ Li Neural Computing and Applications 34 (11), 9087-9101, 2022 | 10 | 2022 |
| Data-efficient protein 3d geometric pretraining via refinement of diffused protein structure decoy Y Huang, L Wu, H Lin, J Zheng, G Wang, SZ Li arXiv preprint arXiv:2302.10888, 2023 | 9 | 2023 |
| Invertible Manifold Learning for Dimension Reduction S Li, H Lin, Z Zang, L Wu, J Xia, SZ Li ECML 2021, 2021 | 7 | 2021 |
| Functional-group-based diffusion for pocket-specific molecule generation and elaboration H Lin, Y Huang, O Zhang, Y Liu, L Wu, S Li, Z Chen, SZ Li Advances in Neural Information Processing Systems 36, 2024 | 5 | 2024 |
| Non-equispaced Fourier Neural Solvers for PDEs H Lin, L Wu, Y Xu, Y Huang, S Li, G Zhao, SZ Li ICLR 2023 Workshop Physics4ML, 2022 | 5 | 2022 |
| Teaching yourself: Graph self-distillation on neighborhood for node classification L Wu, J Xia, H Lin, Z Gao, Z Liu, G Zhao, SZ Li arXiv preprint arXiv:2210.02097, 2022 | 5 | 2022 |
| An Empirical Study: Extensive Deep Temporal Point Process H Lin, C Tan, L Wu, Z Gao, S Li arXiv preprint arXiv:2110.09823, 2021 | 5 | 2021 |
Stan Z. Li (李子青)Westlake University & CAS Institute of AutomationVerified email at westlake.edu.cn
