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RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network

Authors:

Hanghang TongAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 1214 - 1225

https://doi.org/10.1145/3485447.3512169

Published: 25 April 2022 Publication History

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Abstract

Graph Convolutional Network (GCN) plays pivotal roles in many real-world applications. Despite the successes of GCN deployment, GCN often exhibits performance disparity with respect to node degrees, resulting in worse predictive accuracy for low-degree nodes. We formulate the problem of mitigating the degree-related performance disparity in GCN from the perspective of the Rawlsian difference principle, which is originated from the theory of distributive justice. Mathematically, we aim to balance the utility between low-degree nodes and high-degree nodes while minimizing the task-specific loss. Specifically, we reveal the root cause of this degree-related unfairness by analyzing the gradients of weight matrices in GCN. Guided by the gradients of weight matrices, we further propose a pre-processing method RawlsGCN-Graph and an in-processing method RawlsGCN-Grad that achieves fair predictive accuracy in low-degree nodes without modification on the GCN architecture or introduction of additional parameters. Extensive experiments on real-world graphs demonstrate the effectiveness of our proposed RawlsGCN methods in significantly reducing degree-related bias while retaining comparable overall performance.

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Cited By

Chen ARossi RPark NTrivedi PWang YYu TKim SDernoncourt FAhmed N(2024)Fairness-Aware Graph Neural Networks: A SurveyACM Transactions on Knowledge Discovery from Data10.1145/364914218:6(1-23)Online publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1145/3649142
Dong YLei ZZheng ZWang SMa JHuang AChen CLi JChua TNgo CKumar RLauw HKa-Wei Lee R(2024)PyGDebias: A Python Library for Debiasing in Graph LearningCompanion Proceedings of the ACM on Web Conference 202410.1145/3589335.3651239(1019-1022)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651239
He JKang JNargesian FWang HZhang AZhou DChua TNgo CKumar RLauw HKa-Wei Lee R(2024)TrustLOG: The Second Workshop on Trustworthy Learning on GraphsCompanion Proceedings of the ACM on Web Conference 202410.1145/3589335.3641305(1785-1788)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3641305
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Index Terms

RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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Cited By

Chen ARossi RPark NTrivedi PWang YYu TKim SDernoncourt FAhmed N(2024)Fairness-Aware Graph Neural Networks: A SurveyACM Transactions on Knowledge Discovery from Data10.1145/364914218:6(1-23)Online publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1145/3649142
Dong YLei ZZheng ZWang SMa JHuang AChen CLi JChua TNgo CKumar RLauw HKa-Wei Lee R(2024)PyGDebias: A Python Library for Debiasing in Graph LearningCompanion Proceedings of the ACM on Web Conference 202410.1145/3589335.3651239(1019-1022)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651239
He JKang JNargesian FWang HZhang AZhou DChua TNgo CKumar RLauw HKa-Wei Lee R(2024)TrustLOG: The Second Workshop on Trustworthy Learning on GraphsCompanion Proceedings of the ACM on Web Conference 202410.1145/3589335.3641305(1785-1788)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3641305
Liu ZFang YZhang WZhang XHoi S(2024)Locality-Aware Tail Node Embeddings on Homogeneous and Heterogeneous NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.331335536:6(2517-2532)Online publication date: Jun-2024
https://doi.org/10.1109/TKDE.2023.3313355
Zhang HWu BYuan XPan STong HPei J(2024)Trustworthy Graph Neural Networks: Aspects, Methods, and TrendsProceedings of the IEEE10.1109/JPROC.2024.3369017112:2(97-139)Online publication date: Mar-2024
https://doi.org/10.1109/JPROC.2024.3369017
Zhao MJia A(2024)DAHGNKnowledge-Based Systems10.1016/j.knosys.2023.111355285:COnline publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1016/j.knosys.2023.111355
Bashardoust AFriedler SScheidegger CSullivan BVenkatasubramanian S(2023)Reducing Access Disparities in Networks using Edge Augmentation✱Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594105(1635-1651)Online publication date: 12-Jun-2023
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Li MCoşkun MKoyutürk MWang MYoon B(2023)Canonical Representation of Biological Networks Using Graph ConvolutionProceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics10.1145/3584371.3612963(1-9)Online publication date: 3-Sep-2023
https://dl.acm.org/doi/10.1145/3584371.3612963
Yoo HLee YShin KKim S(2023)Disentangling Degree-related Biases and Interest for Out-of-Distribution Generalized Directed Network EmbeddingProceedings of the ACM Web Conference 202310.1145/3543507.3583271(231-239)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583271
Cong ZShi BLi SYang JHe QPei J(2023)FairSample: Training Fair and Accurate Graph Convolutional Neural Networks EfficientlyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.330637836:4(1537-1551)Online publication date: 25-Aug-2023
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