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 | 125 | 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 | 45 | 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 | 22* | 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 | 15 | 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 | 12 | 2023 |
Towards Fair Graph Neural Networks via Graph Counterfactual Z Guo, J Li, T Xiao, Y Ma, S Wang arXiv preprint arXiv:2307.04937, 2023 | 11 | 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 | 10 | 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 | 8 | 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 | 1 | 2024 |
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, 0 | | |
GraphECL: Towards Efficient Contrastive Learning for Graphs T Xiao, H Zhu, Z Zhang, Z Guo, CC Aggarwal, S Wang | | |