| Self-supervised graph co-training for session-based recommendation X Xia, H Yin, J Yu, Y Shao, L Cui Proceedings of the 30th ACM international conference on information …, 2021 | 189 | 2021 |
| NSCaching: simple and efficient negative sampling for knowledge graph embedding Y Zhang, Q Yao, Y Shao, L Chen 2019 IEEE 35th International Conference on Data Engineering (ICDE), 614-625, 2019 | 154 | 2019 |
| Parallel subgraph listing in a large-scale graph Y Shao, B Cui, L Chen, L Ma, J Yao, N Xu Proceedings of the 2014 ACM SIGMOD international conference on Management of …, 2014 | 144 | 2014 |
| RetroMAE: Pre-training retrieval-oriented language models via masked auto-encoder S Xiao, Z Liu, Y Shao, Z Cao arXiv preprint arXiv:2205.12035, 2022 | 139 | 2022 |
| Joint modeling of user check-in behaviors for point-of-interest recommendation H Yin, X Zhou, Y Shao, H Wang, S Sadiq Proceedings of the 24th ACM International on Conference on Information and …, 2015 | 137 | 2015 |
| Heterogeneous hypergraph embedding for graph classification X Sun, H Yin, B Liu, H Chen, J Cao, Y Shao, NQ Viet Hung Proceedings of the 14th ACM international conference on web search and data …, 2021 | 120 | 2021 |
| VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning F Fu, Y Shao, L Yu, J Jiang, H Xue, Y Tao, B Cui Proceedings of the 2021 International Conference on Management of Data, 563-576, 2021 | 100 | 2021 |
| Tornado: A system for real-time iterative analysis over evolving data X Shi, B Cui, Y Shao, Y Tong Proceedings of the 2016 International Conference on Management of Data, 417-430, 2016 | 94 | 2016 |
| Reliable data distillation on graph convolutional network W Zhang, X Miao, Y Shao, J Jiang, L Chen, O Ruas, B Cui Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020 | 86 | 2020 |
| Snapshot boosting: a fast ensemble framework for deep neural networks W Zhang, J Jiang, Y Shao, B Cui Science China Information Sciences 63 (1), 112102, 2020 | 80 | 2020 |
| Don’t waste your bits! squeeze activations and gradients for deep neural networks via tinyscript F Fu, Y Hu, Y He, J Jiang, Y Shao, C Zhang, B Cui International Conference on Machine Learning, 3304-3314, 2020 | 77 | 2020 |
| Efficient cohesive subgraphs detection in parallel Y Shao, L Chen, B Cui Proceedings of the 2014 ACM SIGMOD International Conference on Management of …, 2014 | 70 | 2014 |
| Sancus: staleness-aware communication-avoiding full-graph decentralized training in large-scale graph neural networks J Peng, Z Chen, Y Shao, Y Shen, L Chen, J Cao Proceedings of the VLDB Endowment 15 (9), 1937-1950, 2022 | 69 | 2022 |
| An efficient similarity search framework for SimRank over large dynamic graphs Y Shao, B Cui, L Chen, M Liu, X Xie Proceedings of the VLDB Endowment 8 (8), 838-849, 2015 | 64 | 2015 |
| Heterogeneity-aware distributed machine learning training via partial reduce X Miao, X Nie, Y Shao, Z Yang, J Jiang, L Ma, B Cui Proceedings of the 2021 International Conference on Management of Data, 2262 …, 2021 | 57 | 2021 |
| Heterogeneous environment aware streaming graph partitioning N Xu, B Cui, L Chen, Z Huang, Y Shao IEEE Transactions on Knowledge and Data Engineering 27 (6), 1560-1572, 2014 | 51 | 2014 |
| Efficient automatic CASH via rising bandits Y Li, J Jiang, J Gao, Y Shao, C Zhang, B Cui Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4763-4771, 2020 | 48 | 2020 |
| Fast de-anonymization of social networks with structural information Y Shao, J Liu, S Shi, Y Zhang, B Cui Data Science and Engineering 4 (1), 76-92, 2019 | 45 | 2019 |
| LDA* a robust and large-scale topic modeling system L Yut, C Zhang, Y Shao, B Cui Proceedings of the VLDB Endowment 10 (11), 1406-1417, 2017 | 43 | 2017 |
| Distributed graph neural network training: A survey Y Shao, H Li, X Gu, H Yin, Y Li, X Miao, W Zhang, B Cui, L Chen ACM Computing Surveys 56 (8), 1-39, 2024 | 42 | 2024 |
Bin CUIProfessor of Computer Science, Peking UniversityПотвърден имейл адрес: pku.edu.cn
