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We study the task of node classification for graph neural networks (GNNs) and establish a connection between group fairness, as measured by statistical parity and equal opportunity, and local assortativity, i.e., the tendency of linked nodes to have similar attributes.
Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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Homophily influences ranking of minorities in social networks
Fariba Karimi, Mathieu Génois, Claudia Wagner, Philipp Singer, and Markus Strohmaier · 2018
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Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Fairwalk: Towards fair graph embedding
Tahleen Rahman, Bartlomiej Surma, Michael Backes, and Yang Zhang · 2019
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Simplifying graph convolutional networks
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger · 2019
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Simple and deep graph convolutional networks
Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li · 2020
Cited alongside, same era.
On dyadic fairness: Exploring and mitigating bias in graph connections
Peizhao Li, Yifei Wang, Han Zhao, Pengyu Hong, and Hongfu Liu · 2020
Cited alongside, same era.
Beyond homophily in graph neural networks: Current limitations and effective designs
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra · 2020
Cited alongside, same era.
Towards a unified framework for fair and stable graph representation learning
Chirag Agarwal, Himabindu Lakkaraju, and Marinka Zitnik · 2021
Cited alongside, same era.
Beyond low-frequency information in graph convolutional networks
Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information
Enyan Dai and Suhang Wang · 2021
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Individual fairness for graph neural networks: A ranking based approach
Yushun Dong, Jian Kang, Hanghang Tong, and Jundong Li · 2021
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What’s fair about individual fairness?
Will Fleisher · 2021
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Biased edge dropout for enhancing fairness in graph representation learning
Indro Spinelli, Simone Scardapane, Amir Hussain, and Aurelio Uncini · 2021
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Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks
Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, and Danai Koutra · 2021
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Deyu Bo, Xiao Wang, Chuan Shi, and Huawei Shen · 2021
Cited alongside, same era.
Birds of a feather: Homophily in social networks
Miller McPherson, Lynn Smith-Lovin, and James M Cook
Cited in the paper.
Edits: Modeling and mitigating data bias for graph neural networks
Yushun Dong, Ninghao Liu, Brian Jalaian, and Jundong Li · 2022
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