| 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 | 163 | 2024 |
| Decoupled self-supervised learning for graphs T Xiao, Z Chen, Z Guo, Z Zhuang, S Wang Advances in Neural Information Processing Systems 35, 620-634, 2022 | 51 | 2022 |
| Label-wise graph convolutional network for heterophilic graphs E Dai, S Zhou, Z Guo, S Wang Learning on Graphs Conference, 26: 1-26: 21, 2022 | 31* | 2022 |
| Towards Fair Graph Neural Networks via Graph Counterfactual Z Guo, J Li, T Xiao, Y Ma, S Wang arXiv preprint arXiv:2307.04937, 2023 | 22 | 2023 |
| Counterfactual learning on graphs: A survey Z Guo, Z Wu, T Xiao, C Aggarwal, H Liu, S Wang Machine Intelligence Research 22 (1), 17-59, 2025 | 20 | 2025 |
| On the safety of open-sourced large language models: Does alignment really prevent them from being misused? H Zhang, Z Guo, H Zhu, B Cao, L Lin, J Jia, J Chen, D Wu arXiv preprint arXiv:2310.01581, 2023 | 20 | 2023 |
| Link prediction on heterophilic graphs via disentangled representation learning S Zhou, Z Guo, C Aggarwal, X Zhang, S Wang arXiv preprint arXiv:2208.01820, 2022 | 20 | 2022 |
| Decoupled self-supervised learning for non-homophilous graphs T Xiao, Z Chen, Z Guo, Z Zhuang, S Wang arXiv preprint arXiv:2206.03601, 2022 | 11 | 2022 |
| Fairness-aware message passing for graph neural networks H Zhu, G Fu, Z Guo, Z Zhang, T Xiao, S Wang arXiv preprint arXiv:2306.11132, 2023 | 10 | 2023 |
| Efficient contrastive learning for fast and accurate inference on graphs T Xiao, H Zhu, Z Zhang, Z Guo, CC Aggarwal, S Wang, VG Honavar Forty-first International Conference on Machine Learning, 2024 | 8 | 2024 |
| Jailbreak open-sourced large language models via enforced decoding H Zhang, Z Guo, H Zhu, B Cao, L Lin, J Jia, J Chen, D Wu Proceedings of the 62nd Annual Meeting of the Association for Computational …, 2024 | 7 | 2024 |
| Addressing shortcomings in fair graph learning datasets: Towards a new benchmark X Qian, Z Guo, J Li, H Mao, B Li, S Wang, Y Ma Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and …, 2024 | 4 | 2024 |
| GraphECL: Towards Efficient Contrastive Learning for Graphs T Xiao, H Zhu, Z Zhang, Z Guo, CC Aggarwal, S Wang | | |
