| OOD-GNN: Out-of-Distribution Generalized Graph Neural Network H Li, X Wang, Z Zhang, W Zhu IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021 | 131 | 2021 |
| Billion-scale network embedding with iterative random projection Z Zhang, P Cui, H Li, X Wang, W Zhu IEEE International Conference on Data Mining (ICDM), 787-796, 2018 | 109 | 2018 |
| Learning Invariant Graph Representations for Out-of-Distribution Generalization H Li, Z Zhang, X Wang, W Zhu Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS), 2022 | 106 | 2022 |
| Out-Of-Distribution Generalization on Graphs: A Survey H Li, X Wang, Z Zhang, W Zhu arXiv preprint arXiv:2202.07987, 2022 | 105 | 2022 |
| Disentangled Contrastive Learning on Graphs H Li, X Wang, Z Zhang, Z Yuan, H Li, W Zhu Thirty-Fifth Conference on Neural Information Processing Systems (NeurIPS), 2021 | 104 | 2021 |
| Intention-aware Sequential Recommendation with Structured Intent Transition H Li, X Wang, Z Zhang, J Ma, P Cui, W Zhu IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021 | 73 | 2021 |
| Dynamic Graph Neural Networks Under Spatio-Temporal Distribution Shift Z Zhang, X Wang, Z Zhang, H Li, Z Qin, W Zhu Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS), 2022 | 71 | 2022 |
| LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs? Z Zhang, X Wang, Z Zhang, H Li, Y Qin, W Zhu ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024), 2024 | 40* | 2024 |
| AutoGL: A Library for Automated Graph Learning C Guan, Z Zhang, H Li, H Chang, Z Zhang, Y Qin, J Jiang, X Wang, W Zhu ICLR 2021 Workshop on Geometrical and Topological Representation Learning, 2021 | 37* | 2021 |
| Disentangled Graph Contrastive Learning With Independence Promotion H Li, Z Zhang, X Wang, W Zhu IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022 | 34 | 2022 |
| Curriculum Graph Machine Learning: A Survey H Li, X Wang, W Zhu International Joint Conference on Artificial Intelligence (IJCAI), 2023 | 23 | 2023 |
| Intent-aware recommendation via disentangled graph contrastive learning Y Wang, X Wang, X Huang, Y Yu, H Li, M Zhang, Z Guo, W Wu International Joint Conference on Artificial Intelligence (IJCAI 2023), 2023 | 22 | 2023 |
| Graph Meets LLMs: Towards Large Graph Models Z Zhang, H Li, Z Zhang, Y Qin, X Wang, W Zhu NeurIPS 2023 Workshop: New Frontiers in Graph Learning, 2023 | 20 | 2023 |
| AutoGT: Automated Graph Transformer Architecture Search Z Zhang, X Wang, C Guan, Z Zhang, H Li, W Zhu International Conference on Learning Representations (ICLR 2023), 2023 | 20 | 2023 |
| Spectral invariant learning for dynamic graphs under distribution shifts Z Zhang, X Wang, Z Zhang, Z Qin, W Wen, H Xue, H Li, W Zhu Thirty-Seventh Conference on Neural Information Processing Systems (NeurIPS), 2023 | 16 | 2023 |
| Fates of Microscopic Social Ecosystems: Keep Alive or Dead? H Li, P Cui, C Zang, T Zhang, W Zhu, Y Lin Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 16 | 2019 |
| Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution Generalization T Jia, H Li, C Yang, T Tao, C Shi AAAI Conference on Artificial Intelligence (AAAI 2024), 2024 | 15 | 2024 |
| Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions X Wang, Z Zhang, H Li, W Zhu arXiv preprint arXiv:2201.01288, 2024 | 14 | 2024 |
| Large graph models: A perspective Z Zhang, H Li, Z Zhang, Y Qin, X Wang, W Zhu arXiv preprint arXiv:2308.14522, 2023 | 14 | 2023 |
| Invariant Node Representation Learning under Distribution Shifts with Multiple Latent Environments H Li, Z Zhang, X Wang, W Zhu ACM Transactions on Information Systems (TOIS), 2023 | 13 | 2023 |
Wenwu ZhuProfessor, Computer Science, Tsinghua UniverisityZweryfikowany adres z tsinghua.edu.cn
