| 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 | 1406 | 2019 |
| Deep graph library: Towards efficient and scalable deep learning on graphs MY Wang ICLR workshop on representation learning on graphs and manifolds, 2019 | 891 | 2019 |
| {FlashGraph}: Processing {Billion-Node} graphs on an array of commodity {SSDs} D Zheng, D Mhembere, R Burns, J Vogelstein, CE Priebe, AS Szalay 13th USENIX Conference on File and Storage Technologies (FAST 15), 45-58, 2015 | 296 | 2015 |
| 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, ... In 2020 IEEE/ACM 10th Workshop on Irregular Applications: Architectures and …, 2020 | 282* | 2020 |
| Dgl-ke: Training knowledge graph embeddings at scale D Zheng, X Song, C Ma, Z Tan, Z Ye, J Dong, H Xiong, Z Zhang, ... Proceedings of the 43rd international ACM SIGIR conference on research and …, 2020 | 213 | 2020 |
| Drkg-drug repurposing knowledge graph for covid-19 VN Ioannidis, X Song, S Manchanda, M Li, X Pan, D Zheng, X Ning, ... arXiv preprint arXiv:2010.09600, 2020 | 124 | 2020 |
| Tgl: A general framework for temporal gnn training on billion-scale graphs H Zhou, D Zheng, I Nisa, V Ioannidis, X Song, G Karypis arXiv preprint arXiv:2203.14883, 2022 | 110 | 2022 |
| 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 | 101 | 2020 |
| Distributed hybrid cpu and gpu training for graph neural networks on billion-scale heterogeneous graphs D Zheng, X Song, C Yang, D LaSalle, G Karypis Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 68 | 2022 |
| Collective multi-type entity alignment between knowledge graphs Q Zhu, H Wei, B Sisman, D Zheng, C Faloutsos, XL Dong, J Han Proceedings of the web conference 2020, 2241-2252, 2020 | 66 | 2020 |
| Supervised dimensionality reduction for big data JT Vogelstein, EW Bridgeford, M Tang, D Zheng, C Douville, R Burns, ... Nature communications 12 (1), 2872, 2021 | 64 | 2021 |
| Few-shot link prediction via graph neural networks for covid-19 drug-repurposing VN Ioannidis, D Zheng, G Karypis arXiv preprint arXiv:2007.10261, 2020 | 64 | 2020 |
| Toward millions of file system IOPS on low-cost, commodity hardware D Zheng, R Burns, AS Szalay Proceedings of the international conference on high performance computing …, 2013 | 64 | 2013 |
| Global neighbor sampling for mixed CPU-GPU training on giant graphs J Dong, D Zheng, LF Yang, G Karypis Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 38 | 2021 |
| TraverseNet: Unifying space and time in message passing for traffic forecasting Z Wu, D Zheng, S Pan, Q Gan, G Long, G Karypis IEEE Transactions on Neural Networks and Learning Systems 35 (2), 2003-2013, 2022 | 35 | 2022 |
| A parallel page cache: IOPS and caching for multicore systems D Zheng, R Burns, AS Szalay Proceedings of the 4th USENIX conference on Hot Topics in Storage and File …, 2012 | 34 | 2012 |
| PaGE-Link: Path-based graph neural network explanation for heterogeneous link prediction S Zhang, J Zhang, X Song, S Adeshina, D Zheng, C Faloutsos, Y Sun Proceedings of the ACM Web Conference 2023, 3784-3793, 2023 | 33 | 2023 |
| Train your own gnn teacher: Graph-aware distillation on textual graphs C Mavromatis, VN Ioannidis, S Wang, D Zheng, S Adeshina, J Ma, H Zhao, ... Joint European Conference on Machine Learning and Knowledge Discovery in …, 2023 | 30 | 2023 |
| Dsp: Efficient gnn training with multiple gpus Z Cai, Q Zhou, X Yan, D Zheng, X Song, C Zheng, J Cheng, G Karypis Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and …, 2023 | 30 | 2023 |
| Semi-external memory sparse matrix multiplication for billion-node graphs D Zheng, D Mhembere, V Lyzinski, JT Vogelstein, CE Priebe, R Burns IEEE Transactions on Parallel and Distributed Systems 28 (5), 1470-1483, 2016 | 29 | 2016 |
George KarypisDistinguished McKnight University Professor, University of Minnesota; SPS, AWSOverená e-mailová adresa na: umn.edu
