| Deep graph library: A graph-centric, highly-performant package for graph neural networks M Wang, D Zheng, Z Ye, Q Gan, M Li, X Song, J Zhou, C Ma, L Yu, Y Gai, ... arXiv preprint arXiv:1909.01315, 2019 | 2821* | 2019 |
| DistDGL: Distributed graph neural network training for billion-scale graphs D Zheng, C Ma, M Wang, J Zhou, Q Su, X Song, Q Gan, Z Zhang, ... 2020 IEEE/ACM 10th Workshop on Irregular Applications: Architectures and …, 2020 | 245 | 2020 |
| Dgl-lifesci: An open-source toolkit for deep learning on graphs in life science M Li, J Zhou, J Hu, W Fan, Y Zhang, Y Gu, G Karypis ACS omega 6 (41), 27233-27238, 2021 | 230 | 2021 |
| Qidong Su, Xiang Song, Quan Gan, Zheng Zhang, and George Karypis. 2020. DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs CM Da Zheng, M Wang, J Zhou arXiv preprint arXiv:2010.05337, 2020 | 153 | 2020 |
| Graph neural networks inspired by classical iterative algorithms Y Yang, T Liu, Y Wang, J Zhou, Q Gan, Z Wei, Z Zhang, Z Huang, D Wipf International Conference on Machine Learning, 11773-11783, 2021 | 114 | 2021 |
| Dgi: An easy and efficient framework for gnn model evaluation P Yin, X Yan, J Zhou, Q Fu, Z Cai, J Cheng, B Tang, M Wang Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 28* | 2023 |
