87 citations · 343 across the 20 of their papers we have counts for
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cs.LG2022★ 87 cited
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
Enyan Dai, Tianxiang Zhao, Huaisheng Zhu +5
Graph Neural Networks (GNNs) have made rapid developments in the recent years. Due to their great ability in modeling graph-structured data, GNNs are vastly used in various applica…
cs.LG2022★ 1 cited
Learning Fair Models without Sensitive Attributes: A Generative Approach
Huaisheng Zhu, Enyan Dai, Hui Liu +1
Most existing fair classifiers rely on sensitive attributes to achieve fairness. However, for many scenarios, we cannot obtain sensitive attributes due to privacy and legal issues.…
cs.LG2022★ 9 cited
Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels
Enyan Dai, Wei Jin, Hui Liu +1
Graph Neural Networks (GNNs) have shown their great ability in modeling graph structured data. However, real-world graphs usually contain structure noises and have limited labeled…