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| 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 | 879 | 2019 |
| Deep graph library: Towards efficient and scalable deep learning on graphs MY Wang ICLR workshop on representation learning on graphs and manifolds, 2019 | 630 | 2019 |
| Supporting very large models using automatic dataflow graph partitioning M Wang, C Huang, J Li Proceedings of the Fourteenth EuroSys Conference 2019, 1-17, 2019 | 126 | 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 | 89 | 2020 |
| Distdgl: distributed graph neural network training for billion-scale graphs CM Da Zheng, M Wang, J Zhou, Q Su, X Song, Q Gan, Z Zhang, G Karypis CoRR, 2020 | 86 | 2020 |
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| Featgraph: A flexible and efficient backend for graph neural network systems Y Hu, Z Ye, M Wang, J Yu, D Zheng, M Li, Z Zhang, Z Zhang, Y Wang SC20: International Conference for High Performance Computing, Networking …, 2020 | 65 | 2020 |
| Minerva: A scalable and highly efficient training platform for deep learning M Wang, T Xiao, J Li, J Zhang, C Hong, Z Zhang NIPS Workshop, Distributed Machine Learning and Matrix Computations, 51, 2014 | 31 | 2014 |
| Deep graph library: towards efficient and scalable deep learning on graphs. CoRR abs/1909.01315 (2019) M Wang, L Yu, QG Da Zheng, Y Gai, Z Ye, M Li, J Zhou, Q Huang, C Ma, ... arXiv preprint arXiv:1909.01315, 2019 | 26 | 2019 |
| Prediction of cutting force in five-axis flat-end milling ZC Wei, ML Guo, MJ Wang, SQ Li, SX Liu The International Journal of Advanced Manufacturing Technology 96, 137-152, 2018 | 25 | 2018 |
| Study on design and experiments of extrusion die for polypropylene single-lumen micro tubes GB Jin, DY Zhao, MJ Wang, YF Jin, HQ Tian, J Zhang Microsystem Technologies 21, 2495-2503, 2015 | 25 | 2015 |
| Impression store: Compressive sensing-based storage for big data analytics J Zhang, Y Yan, LJ Chen, M Wang, T Moscibroda, Z Zhang 6th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 14), 2014 | 25 | 2014 |
| Scalable graph neural networks with deep graph library D Zheng, M Wang, Q Gan, X Song, Z Zhang, G Karypis Proceedings of the 14th ACM International Conference on Web Search and Data …, 2021 | 21 | 2021 |
| Unifying data, model and hybrid parallelism in deep learning via tensor tiling M Wang, C Huang, J Li arXiv preprint arXiv:1805.04170, 2018 | 21 | 2018 |
| Graphiler: Optimizing graph neural networks with message passing data flow graph Z Xie, M Wang, Z Ye, Z Zhang, R Fan Proceedings of Machine Learning and Systems 4, 515-528, 2022 | 17 | 2022 |
| Force predictive model for five-axis ball end milling of sculptured surface ZC Wei, ML Guo, MJ Wang, SQ Li, SX Liu The International Journal of Advanced Manufacturing Technology 98, 1367-1377, 2018 | 15 | 2018 |
| A review of microstructural evolution in the adiabatic shear bands induced by high speed machining C Duan, M Wang Acta Metallurgica Sinica (English Letters) 26, 97-112, 2013 | 15 | 2013 |
| Learning graph neural networks with deep graph library D Zheng, M Wang, Q Gan, Z Zhang, G Karypis Companion Proceedings of the Web Conference 2020, 305-306, 2020 | 14 | 2020 |
| Slicing parameters optimizing and experiments based on constant wire wear loss model in multi-wire saw Z Li, MJ Wang, X Pan, YM Ni The International Journal of Advanced Manufacturing Technology 81, 329-334, 2015 | 14 | 2015 |
