| Kineograph: taking the pulse of a fast-changing and connected world R Cheng, J Hong, A Kyrola, Y Miao, X Weng, M Wu, F Yang, L Zhou, ... Proceedings of the 7th ACM european conference on Computer Systems, 85-98, 2012 | 358 | 2012 |
| {NeuGraph}: Parallel deep neural network computation on large graphs L Ma, Z Yang, Y Miao, J Xue, M Wu, L Zhou, Y Dai 2019 USENIX Annual Technical Conference (USENIX ATC 19), 443-458, 2019 | 283 | 2019 |
| Chronos: a graph engine for temporal graph analysis W Han, Y Miao, K Li, M Wu, F Yang, L Zhou, V Prabhakaran, W Chen, ... Proceedings of the Ninth European Conference on Computer Systems, 1-14, 2014 | 252 | 2014 |
| Pagraph: Scaling gnn training on large graphs via computation-aware caching Z Lin, C Li, Y Miao, Y Liu, Y Xu Proceedings of the 11th ACM Symposium on Cloud Computing, 401-415, 2020 | 173 | 2020 |
| GraM: scaling graph computation to the trillions M Wu, F Yang, J Xue, W Xiao, Y Miao, L Wei, H Lin, Y Dai, L Zhou Proceedings of the Sixth ACM Symposium on Cloud Computing, 408-421, 2015 | 157 | 2015 |
| Rammer: Enabling holistic deep learning compiler optimizations with {rTasks} L Ma, Z Xie, Z Yang, J Xue, Y Miao, W Cui, W Hu, F Yang, L Zhang, ... 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2020 | 151 | 2020 |
| Immortalgraph: A system for storage and analysis of temporal graphs Y Miao, W Han, K Li, M Wu, F Yang, L Zhou, V Prabhakaran, E Chen, ... ACM Transactions on Storage (TOS) 11 (3), 1-34, 2015 | 102 | 2015 |
| {Tux²}: Distributed Graph Computation for Machine Learning W Xiao, J Xue, Y Miao, Z Li, C Chen, M Wu, W Li, L Zhou 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI …, 2017 | 80 | 2017 |
| Platform for continuous graph update and computation F Yang, L Zhou, M Wu, A Kyrola, R Cheng, Y Miao, X Weng, J Hong US Patent 9,244,983, 2016 | 69 | 2016 |
| Fast distributed deep learning over rdma J Xue, Y Miao, C Chen, M Wu, L Zhang, L Zhou Proceedings of the Fourteenth EuroSys Conference 2019, 1-14, 2019 | 65 | 2019 |
| Architectural implications of graph neural networks Z Zhang, J Leng, L Ma, Y Miao, C Li, M Guo IEEE Computer architecture letters 19 (1), 59-62, 2020 | 58 | 2020 |
| Breaking the computation and communication abstraction barrier in distributed machine learning workloads A Jangda, J Huang, G Liu, AHN Sabet, S Maleki, Y Miao, M Musuvathi, ... Proceedings of the 27th ACM International Conference on Architectural …, 2022 | 45 | 2022 |
| Efficient data loader for fast sampling-based gnn training on large graphs Y Bai, C Li, Z Lin, Y Wu, Y Miao, Y Liu, Y Xu IEEE Transactions on Parallel and Distributed Systems 32 (10), 2541-2556, 2021 | 37 | 2021 |
| Motif-Preserving Temporal Network Embedding. H Huang, Z Fang, X Wang, Y Miao, H Jin IJCAI, 1237-1243, 2020 | 37 | 2020 |
| Towards efficient large-scale graph neural network computing L Ma, Z Yang, Y Miao, J Xue, M Wu, L Zhou, Y Dai arXiv preprint arXiv:1810.08403, 2018 | 35 | 2018 |
| Evomoe: An evolutional mixture-of-experts training framework via dense-to-sparse gate X Nie, X Miao, S Cao, L Ma, Q Liu, J Xue, Y Miao, Y Liu, Z Yang, B Cui arXiv preprint arXiv:2112.14397, 2021 | 30 | 2021 |
| Dense-to-sparse gate for mixture-of-experts X Nie, S Cao, X Miao, L Ma, J Xue, Y Miao, Z Yang, Z Yang, CUI Bin | 27 | 2021 |
| Platform for continuous graph update and computation F Yang, A Kyrola, X Weng, R Cheng, M Wu, J Hong, L Zhou, Y Miao US Patent 9,589,069, 2017 | 16 | 2017 |
| Accelerating gnn training with locality-aware partial execution T Kim, C Hwang, KS Park, Z Lin, P Cheng, Y Miao, L Ma, Y Xiong Proceedings of the 12th ACM SIGOPS Asia-Pacific Workshop on Systems, 34-41, 2021 | 10 | 2021 |
| Distributed graph computation meets machine learning W Xiao, J Xue, Y Miao, Z Li, C Chen, M Wu, W Li, L Zhou IEEE Transactions on Parallel and Distributed Systems 31 (7), 1588-1604, 2020 | 9 | 2020 |
