| 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 | 253 | 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 arXiv preprint arXiv:2204.08570, 2022 | 98 | 2022 |
| 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 | 42 | 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 | 41 | 2022 |
| Exploring edge disentanglement for node classification T Zhao, X Zhang, S Wang Proceedings of the ACM Web Conference 2022, 1028-1036, 2022 | 26 | 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 | 25 | 2020 |
| 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 | 21 | 2020 |
| 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 | 20 | 2022 |
| 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 | 19 | 2021 |
| 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 | 15 | 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 |
| 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 | 9 | 2022 |
| 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 | 7 | 2023 |
| On consistency in graph neural network interpretation T Zhao, D Luo, X Zhang, S Wang arXiv preprint arXiv:2205.13733 9, 2022 | 7 | 2022 |
| Zero-shot learning: An energy based approach T Zhao, G Liu, C Ma, E Chen 2018 IEEE International Conference on Data Mining (ICDM), 797-806, 2018 | 6 | 2018 |
| Synthetic over-sampling for imbalanced node classification with graph neural networks T Zhao, X Zhang, S Wang arXiv preprint arXiv:2206.05335, 2022 | 5 | 2022 |
| A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy E Dai, T Zhao, H Zhu, J Xu, Z Guo, H Liu, J Tang, S Wang Robustness, Fairness, and Explainability. arXiv 2204, 2022 | 5 | 2022 |
| 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 | 4 | 2023 |
| Fair and effective policing for neighborhood safety: understanding and overcoming selection biases W Ren, K Liu, T Zhao, Y Fu Frontiers in big data 4, 787459, 2021 | 3 | 2021 |
| 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 | 1 | 2024 |
