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Zhimeng Guo
Zhimeng Guo
在 psu.edu 的电子邮件经过验证 - 首页
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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
1252022
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
452022
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
152023
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
122023
Towards Fair Graph Neural Networks via Graph Counterfactual
Z Guo, J Li, T Xiao, Y Ma, S Wang
arXiv preprint arXiv:2307.04937, 2023
112023
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
102022
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
82023
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
12024
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
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