| Towards graph foundation models: A survey and beyond J Liu, C Yang, Z Lu, J Chen, Y Li, M Zhang, T Bai, Y Fang, L Sun, PS Yu, ... arXiv preprint arXiv:2310.11829, 2023 | 68 | 2023 |
| Space4hgnn: a novel, modularized and reproducible platform to evaluate heterogeneous graph neural network T Zhao, C Yang, Y Li, Q Gan, Z Wang, F Liang, H Zhao, Y Shao, X Wang, ... Proceedings of the 45th International ACM SIGIR Conference on Research and …, 2022 | 41 | 2022 |
| A generalized neural diffusion framework on graphs Y Li, X Wang, H Liu, C Shi Proceedings of the AAAI Conference on Artificial Intelligence 38 (8), 8707-8715, 2024 | 9 | 2024 |
| Graph Fairness Learning under Distribution Shifts Y Li, X Wang, Y Xing, S Fan, R Wang, Y Liu, C Shi Proceedings of the ACM on Web Conference 2024, 676-684, 2024 | 6 | 2024 |
| Less is More: on the Over-Globalizing Problem in Graph Transformers Y Xing, X Wang, Y Li, H Huang, C Shi arXiv preprint arXiv:2405.01102, 2024 | 5 | 2024 |
| Towards adaptive information fusion in graph convolutional networks M Zhu, X Wang, C Shi, Y Li, J Du IEEE Transactions on Knowledge and Data Engineering 35 (12), 13055-13069, 2023 | 2 | 2023 |
| FineMolTex: Towards Fine-grained Molecular Graph-Text Pre-training Y Li, Y Fang, M Zhang, C Shi arXiv preprint arXiv:2409.14106, 2024 | | 2024 |
