| 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 | 100 | 2022 |
| 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 | 33 | 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 | 12* | 2022 |
| Counterfactual learning on graphs: A survey Z Guo, T Xiao, Z Wu, C Aggarwal, H Liu, S Wang arXiv preprint arXiv:2304.01391, 2023 | 10 | 2023 |
| 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 | 8 | 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 | 8 | 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 | 5 | 2023 |
| 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 | 5 | 2023 |
| 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 arXiv preprint arXiv:2403.06017, 2024 | | 2024 |
| GraphECL: Towards Efficient Contrastive Learning for Graphs T Xiao, H Zhu, Z Zhang, Z Guo, CC Aggarwal, S Wang | | 2023 |
