| Spherical Message Passing for 3D Molecular Graphs Y Liu*, L Wang*, M Liu, Y Lin, X Zhang, B Oztekin, S Ji International Conference on Learning Representations (ICLR 2022), 2022 | 301* | 2022 |
| 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, ... Journal of Machine Learning Research 22 (240), 1-9, 2021 | 136* | 2021 |
| Advanced graph and sequence neural networks for molecular property prediction and drug discovery Z Wang*, M Liu*, Y Luo*, Z Xu*, Y Xie*, L Wang*, L Cai*, Q Qi, Z Yuan, ... Bioinformatics, 2022 | 116 | 2022 |
| 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, ... arXiv preprint arXiv:2307.08423, 2023 | 103 | 2023 |
| GOOD: A Graph Out-of-Distribution Benchmark S Gui*, X Li*, L Wang, S Ji NeurIPS 2022 Track on Datasets and Benchmarks, 2022 | 103 | 2022 |
| ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs L Wang*, Y Liu*, Y Lin, H Liu, S Ji Advances in Neural Information Processing Systems (NeurIPS 2022), 2022 | 87 | 2022 |
| Learning Hierarchical Protein Representations via Complete 3D Graph Networks L Wang*, H Liu*, Y Liu*, J Kurtin, S Ji International Conference on Learning Representations (ICLR 2023), 2023 | 60* | 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 (ICML 2022), 2022 | 31 | 2022 |
| A Latent Diffusion Model for Protein Structure Generation C Fu*, K Yan*, L Wang, WY Au, M McThrow, T Komikado, K Maruhashi, ... The Second Learning on Graphs Conference (LoG 2023), 2023 | 25* | 2023 |
| A new perspective on building efficient and expressive 3D equivariant graph neural networks W Du*, Y Du*, L Wang*, D Feng, G Wang, S Ji, C Gomes, ZM Ma Advances in Neural Information Processing Systems (NeurIPS 2023), 2023 | 25* | 2023 |
| Fast quantum property prediction via deeper 2d and 3d graph networks M Liu*, C Fu*, X Zhang, L Wang, Y Xie, H Yuan, Y Luo, Z Xu, S Xu, S Ji NeurIPS 2021 AI for Science Workshop, 2021 | 12 | 2021 |
| Development of Xanthene‐Based Fluorescent Dyes: Machine Learning‐Assisted Prediction vs. TD‐DFT Prediction and Experimental Validation Y Wang*, L Cai*, W Chen, D Wang, S Xu, L Wang, MA Kononov, S Ji, ... Chemistry‐Methods 1 (8), 389-396, 2021 | 7 | 2021 |
| Geometry Informed Tokenization of Molecules for Language Model Generation X Li, L Wang, Y Luo, C Edwards, S Gui, Y Lin, H Ji, S Ji arXiv preprint arXiv:2408.10120, 2024 | 1 | 2024 |
| Staleness-based subgraph sampling for large-scale GNNs training L Wang, S Zhang, H Zeng, H Wu, Z Hua, K Hassani, A Malevich, B Long, ... | 1 | 2024 |
| Learning Graph Quantized Tokenizers for Transformers L Wang, K Hassani, S Zhang, D Fu, B Yuan, W Cong, Z Hua, H Wu, N Yao, ... arXiv preprint arXiv:2410.13798, 2024 | | 2024 |
| Language Models are Graph Learners Z Xu, K Hassani, S Zhang, H Zeng, M Yasunaga, L Wang, D Fu, N Yao, ... arXiv preprint arXiv:2410.02296, 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 |
Shuiwang Ji, Professor and Truchard...Department of Computer Science & Engineering, Texas A&M UniversityVerified email at tamu.edu
