| What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders J Li, R Wu, W Sun, L Chen, S Tian, L Zhu, C Meng, Z Zheng, W Wang Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 51* | 2023 |
| Self-supervised Representation Learning on Dynamic Graphs S Tian, R Wu, L Shi, L Zhu, T Xiong Proceedings of the 30th ACM International Conference on Information …, 2021 | 38 | 2021 |
| Subgroup analysis with time‐to‐event data under a logistic‐Cox mixture model R Wu, M Zheng, W Yu Scandinavian journal of statistics 43 (3), 863-878, 2016 | 29 | 2016 |
| Can Social Notifications Help to Mitigate Payment Delinquency in Online Peer‐to‐Peer Lending? X Lu, T Lu, C Wang, R Wu Production and Operations Management 30 (8), 2564-2585, 2021 | 24 | 2021 |
| Scaling up dynamic graph representation learning via spiking neural networks J Li, Z Yu, Z Zhu, L Chen, Q Yu, Z Zheng, S Tian, R Wu, C Meng Proceedings of the AAAI Conference on Artificial Intelligence 37 (7), 8588-8596, 2023 | 14 | 2023 |
| Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs J Li, S Tian, R Wu, L Zhu, W Zhao, C Meng, L Chen, Z Zheng, H Yin arXiv preprint arXiv:2305.10673, 2023 | 6 | 2023 |
| Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embedding Y Hu, W Liang, R Wu, K Xiao, W Wang, X Li, J Liu, Z Qin Proceedings of the ACM Web Conference 2023, 2306-2317, 2023 | 5 | 2023 |
| Guard: Graph universal adversarial defense J Li, J Liao, R Wu, L Chen, Z Zheng, J Dan, C Meng, W Wang Proceedings of the 32nd ACM International Conference on Information and …, 2023 | 3 | 2023 |
| FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks Q Pan, R Wu, T Liu, T Zhang, Y Zhu, W Wang arXiv preprint arXiv:2309.09517, 2023 | 3 | 2023 |
| A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks J Li, H Zhang, R Wu, Z Zhu, L Chen, Z Zheng, B Wang, C Meng arXiv preprint arXiv:2305.19306, 2023 | 3 | 2023 |
| GRANDE: a neural model over directed multigraphs with application to anti-money laundering R Wu, B Ma, H Jin, W Zhao, W Wang, T Zhang 2022 IEEE International Conference on Data Mining (ICDM), 558-567, 2022 | 3 | 2022 |
| Memory Augmented Design of Graph Neural Networks T Xiong, L Zhu, R Wu, Y Qi | 3 | 2020 |
| Privacy-preserving design of graph neural networks with applications to vertical federated learning R Wu, M Zhang, L Lyu, X Xu, X Hao, X Fu, T Liu, T Zhang, W Wang arXiv preprint arXiv:2310.20552, 2023 | 2 | 2023 |
| Estimation and variable selection for semiparametric transformation models under a more efficient cohort sampling design M Wu, M Zheng, W Yu, R Wu Test 27, 570-596, 2018 | 2 | 2018 |
| Scaling Up, Scaling Deep: Blockwise Graph Contrastive Learning J Li, W Sun, R Wu, Y Zhu, L Chen, Z Zheng arXiv preprint arXiv:2306.02117, 2023 | 1 | 2023 |
| DEDGAT: Dual Embedding of Directed Graph Attention Networks for Detecting Financial Risk J Wu, M Yao, D Wu, M Chi, B Wang, R Wu, X Fu, C Meng, W Wang arXiv preprint arXiv:2303.03933, 2023 | 1 | 2023 |
| METHODS AND APPARATUSES FOR ESTIMATING WORD SEGMENT FREQUENCY IN DIFFERENTIAL PRIVACY PROTECTION DATA R Wu, L Shi, Y Chen, Y Zhu US Patent App. 18/275,995, 2024 | | 2024 |
| Privacy-preserving graphical model training methods, apparatuses, and devices R Wu US Patent App. 18/523,090, 2024 | | 2024 |
| On provable privacy vulnerabilities of graph representations R Wu, G Fang, Q Pan, M Zhang, T Liu, W Wang, W Zhao arXiv preprint arXiv:2402.04033, 2024 | | 2024 |
| Neural Frailty Machine R Wu, J Qiao, M Wu, W Yu, M Zheng, T Liu, T Zhang, W Wang | | 2024 |
