| Graphsmote: Imbalanced node classification on graphs with graph neural networks T Zhao, X Zhang, S Wang Proceedings of the 14th ACM international conference on web search and data …, 2021 | 388 | 2021 |
| A comprehensive survey on trustworthy graph neural networks: Privacy, robustness, fairness, and explainability E Dai, T Zhao, H Zhu, J Xu, Z Guo, H Liu, J Tang, S Wang Machine Intelligence Research 21 (6), 1011-1061, 2024 | 161 | 2024 |
| Times series forecasting for urban building energy consumption based on graph convolutional network Y Hu, X Cheng, S Wang, J Chen, T Zhao, E Dai Applied Energy 307, 118231, 2022 | 88 | 2022 |
| Towards fair classifiers without sensitive attributes: Exploring biases in related features T Zhao, E Dai, K Shu, S Wang Proceedings of the Fifteenth ACM International Conference on Web Search and …, 2022 | 71 | 2022 |
| Explanation guided contrastive learning for sequential recommendation L Wang, EP Lim, Z Liu, T Zhao Proceedings of the 31st ACM international conference on information …, 2022 | 43 | 2022 |
| Exploring edge disentanglement for node classification T Zhao, X Zhang, S Wang Proceedings of the ACM Web Conference 2022, 1028-1036, 2022 | 41 | 2022 |
| Semi-supervised graph-to-graph translation T Zhao, X Tang, X Zhang, S Wang Proceedings of the 29th ACM International Conference on Information …, 2020 | 30 | 2020 |
| Analyzing and reducing catastrophic forgetting in parameter efficient tuning W Ren, X Li, L Wang, T Zhao, W Qin arXiv preprint arXiv:2402.18865, 2024 | 28 | 2024 |
| You can still achieve fairness without sensitive attributes: Exploring biases in non-sensitive features T Zhao, E Dai, K Shu, S Wang arXiv preprint arXiv:2104.14537, 2021 | 24 | 2021 |
| Balancing quality and human involvement: An effective approach to interactive neural machine translation T Zhao, L Liu, G Huang, H Li, Y Liu, L GuiQuan, S Shi Proceedings of the AAAI conference on artificial intelligence 34 (05), 9660-9667, 2020 | 22 | 2020 |
| Towards faithful and consistent explanations for graph neural networks T Zhao, D Luo, X Zhang, S Wang Proceedings of the Sixteenth ACM International Conference on Web Search and …, 2023 | 19 | 2023 |
| Tracking and forecasting dynamics in crowdfunding: A basis-synthesis approach X Ren, L Xu, T Zhao, C Zhu, J Guo, E Chen 2018 IEEE International Conference on Data Mining (ICDM), 1212-1217, 2018 | 14 | 2018 |
| Towards inductive and efficient explanations for graph neural networks D Luo, T Zhao, W Cheng, D Xu, F Han, W Yu, X Liu, H Chen, X Zhang IEEE Transactions on Pattern Analysis and Machine Intelligence 46 (8), 5245-5259, 2024 | 12 | 2024 |
| Topoimb: Toward topology-level imbalance in learning from graphs T Zhao, D Luo, X Zhang, S Wang Learning on Graphs Conference, 37: 1-37: 18, 2022 | 12 | 2022 |
| Distribution consistency based self-training for graph neural networks with sparse labels F Wang, T Zhao, S Wang Proceedings of the 17th ACM International Conference on Web Search and Data …, 2024 | 11 | 2024 |
| Skill disentanglement for imitation learning from suboptimal demonstrations T Zhao, W Yu, S Wang, L Wang, X Zhang, Y Chen, Y Liu, W Cheng, ... Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 10 | 2023 |
| On consistency in graph neural network interpretation T Zhao, D Luo, X Zhang, S Wang arXiv preprint arXiv:2205.13733 9, 2022 | 9 | 2022 |
| Disambiguated node classification with graph neural networks T Zhao, X Zhang, S Wang Proceedings of the ACM Web Conference 2024, 914-923, 2024 | 7 | 2024 |
| Faithful and consistent graph neural network explanations with rationale alignment T Zhao, D Luo, X Zhang, S Wang ACM Transactions on Intelligent Systems and Technology 14 (5), 1-23, 2023 | 7 | 2023 |
| Synthetic over-sampling for imbalanced node classification with graph neural networks T Zhao, X Zhang, S Wang arXiv preprint arXiv:2206.05335, 2022 | 7 | 2022 |
