RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network-Reference-Cited by-同舟云学术

RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network

Published:2022-04-25 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ACM Web Conference 2022
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Author:

Kang Jian¹,Zhu Yan²,Xia Yinglong²,Luo Jiebo³,Tong Hanghang¹

Affiliation:

1. University of Illinois at Urbana-Champaign, USA

2. Facebook AI, USA

3. University of Rochester, USA and Facebook AI, USA

Funder

NSF (National Science Foundation)

Army Research Office

Defense Advanced Research Projects Agency

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3485447.3512169

Reference32 articles.

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2. James Atwood and Don Towsley. 2016. Diffusion-Convolutional Neural Networks. In Advances in Neural Information Processing Systems. 1993–2001. James Atwood and Don Towsley. 2016. Diffusion-Convolutional Neural Networks. In Advances in Neural Information Processing Systems. 1993–2001.

3. 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.

4. Joan Bruna Wojciech Zaremba Arthur Szlam and Yann LeCun. 2013. Spectral Networks and Locally Connected Networks on Graphs. arXiv preprint arXiv:1312.6203(2013). Joan Bruna Wojciech Zaremba Arthur Szlam and Yann LeCun. 2013. Spectral Networks and Locally Connected Networks on Graphs. arXiv preprint arXiv:1312.6203(2013).

5. Maarten Buyl and Tijl De Bie . 2020 . DeBayes: A Bayesian Method for Debiasing Network Embeddings. In International Conference on Machine Learning. 1220–1229 . Maarten Buyl and Tijl De Bie. 2020. DeBayes: A Bayesian Method for Debiasing Network Embeddings. In International Conference on Machine Learning. 1220–1229.

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