machine-learning-deep-learning/Awesome-Trustworthy-GNNs
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@@ -32,7 +32,7 @@ This is a collection of resources related to trustworthy graph neural networks.
## Related concepts
### Trustworthy GNNs
1. **Trustworthy Graph Neural Networks: Aspects, Methods and Trends.** *He Zhang, Bang Wu, Xingliang Yuan, Shirui Pan, Hanghang Tong, Jian Pei.* 2022. [paper](https://arxiv.org/abs/2205.07424)
1. **Trustworthy Graph Neural Networks: Aspects, Methods and Trends.** *He Zhang, Bang Wu, Xingliang Yuan, Shirui Pan, Hanghang Tong, Jian Pei.* Proceedings of the IEEE, 2024. [paper](https://arxiv.org/abs/2205.07424)
2. **A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability.** *Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, Junjie Xu, Zhimeng Guo, Hui Liu, Jiliang Tang, Suhang Wang.* 2022. [paper](https://arxiv.org/abs/2204.08570)
### Graph Neural Networks
@@ -290,13 +290,9 @@ If you need more details, please visit the [Survey on Trustworthy GNNs](https://
Hanghang Tong and
Jian Pei},
title = {Trustworthy Graph Neural Networks: Aspects, Methods and Trends},
journal = {CoRR},
volume = {abs/2205.07424},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2205.07424},
doi = {10.48550/arXiv.2205.07424},
eprinttype = {arXiv},
eprint = {2205.07424}
journal = {Proceedings of the IEEE},
year = {2024},
doi = {10.1109/JPROC.2024.3369017},
}
```