Explainability in graph neural networks: A taxonomic survey H Yuan, H Yu, S Gui, S Ji IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 | 580 | 2022 |
On explainability of graph neural networks via subgraph explorations H Yuan, H Yu, J Wang, K Li, S Ji International Conference on Machine Learning, 12241-12252, 2021 | 339 | 2021 |
Dig: A turnkey library for diving into graph deep learning research M Liu, Y Luo, L Wang, Y Xie, H Yuan, S Gui, H Yu, Z Xu, J Zhang, Y Liu, ... Journal of Machine Learning Research, 2021 | 121* | 2021 |
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems X Zhang, L Wang, J Helwig, Y Luo, C Fu, Y Xie, M Liu, Y Lin, Z Xu, K Yan, ... arXiv preprint arXiv:2307.08423, 2023 | 64 | 2023 |
GraphFM: Improving Large-Scale GNN Training via Feature Momentum H Yu, L Wang, B Wang, M Liu, T Yang, S Ji International Conference on Machine Learning, 25684-25701, 2022 | 24 | 2022 |
Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian H Yu, Z Xu, X Qian, X Qian, S Ji Proceedings of the 40th International Conference on Machine Learning 12 …, 2023 | 16 | 2023 |
Qh9: A quantum hamiltonian prediction benchmark for qm9 molecules H Yu, M Liu, Y Luo, A Strasser, X Qian, X Qian, S Ji Advances in Neural Information Processing Systems 36, 2024 | 9 | 2024 |
Your neighbors are communicating: Towards powerful and scalable graph neural networks M Liu, H Yu, S Ji | 3 | 2022 |
NetInfoF Framework: Measuring and Exploiting Network Usable Information MC Lee, H Yu, J Zhang, VN Ioannidis, X Song, S Adeshina, D Zheng, ... arXiv preprint arXiv:2402.07999, 2024 | | 2024 |
Frontiers of Graph Neural Networks with DIG S Ji, M Liu, Y Liu, Y Luo, L Wang, Y Xie, Z Xu, H Yu Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | | 2022 |