Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage-Reference-Cited by-同舟云学术

Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage

Published:2022-08-14 Issue: Volume: Page:
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Container-title:Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Wang Yu¹,Zhao Yuying¹,Dong Yushun²,Chen Huiyuan³,Li Jundong²,Derr Tyler¹

Affiliation:

1. Vanderbilt University, Nashville, TN, USA

2. University of Virginia, Charlottesville, VA, USA

3. Case Western Reserve University, Cleveland, OH, USA

Funder

the Cisco Faculty Research Award

the National Science Foundation (NSF)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3534678.3539404

Reference36 articles.

1. Chirag Agarwal Himabindu Lakkaraju and Marinka Zitnik. 2021. Towards a unified framework for fair and stable graph representation learning. In Uncertainty in Artificial Intelligence. PMLR 2114--2124. Chirag Agarwal Himabindu Lakkaraju and Marinka Zitnik. 2021. Towards a unified framework for fair and stable graph representation learning. In Uncertainty in Artificial Intelligence. PMLR 2114--2124.

2. Avishek Bose and William Hamilton . 2019 . Compositional fairness constraints for graph embeddings . In International Conference on Machine Learning. 715--724 . Avishek Bose and William Hamilton. 2019. Compositional fairness constraints for graph embeddings. In International Conference on Machine Learning. 715--724.

3. Structured Graph Convolutional Networks with Stochastic Masks for Recommender Systems

4. Ming Chen , Zhewei Wei , Zengfeng Huang , Bolin Ding , and Yaliang Li . 2020 . Simple and Deep Graph Convolutional Networks . In Proceedings of the 37th International Conference on Machine Learning, ICML 2020. Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. 2020. Simple and Deep Graph Convolutional Networks. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020.

5. Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information

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