Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark
Author:
Affiliation:
1. Rensselaer Polytechnic Institute, Troy, NY, USA
2. The Pennsylvania State University, University Park, PA, USA
3. New Jersey Institute of Technology, Newark, NJ, USA
4. Michigan State University, East Lansing, MI, USA
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3637528.3671616
Reference34 articles.
1. Chirag Agarwal Himabindu Lakkaraju and Marinka Zitnik. 2021. Towards a Unified Framework for Fair and Stable Graph Representation Learning. arxiv: 2102.13186 [cs.LG]
2. Siddhant Arora. 2020. A survey on graph neural networks for knowledge graph completion. arXiv preprint arXiv:2007.12374 (2020).
3. Enyan Dai Limeng Cui Zhengyang Wang Xianfeng Tang Yinghan Wang Monica Cheng Bing Yin and Suhang Wang. 2023. A unified framework of graph information bottleneck for robustness and membership privacy. In KDD.
4. Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information
5. Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, Junjie Xu, Zhimeng Guo, Hui Liu, Jiliang Tang, and Suhang Wang. [n.,d.]. A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability., Vol. 1, 1 ( [n.,d.]).
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