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NRGNN: Learning a Label Noise Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs

Authors:

Charu Aggarwal,

Suhang WangAuthors Info & Claims

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

Pages 227 - 236

https://doi.org/10.1145/3447548.3467364

Published: 14 August 2021 Publication History

Abstract

Graph Neural Networks (GNNs) have achieved promising results for semi-supervised learning tasks on graphs such as node classification. Despite the great success of GNNs, many real-world graphs are often sparsely and noisily labeled, which could significantly degrade the performance of GNNs, as the noisy information could propagate to unlabeled nodes via graph structure. Thus, it is important to develop a label noise-resistant GNN for semi-supervised node classification. Though extensive studies have been conducted to learn neural networks with noisy labels, they mostly focus on independent and identically distributed data and assume a large number of noisy labels are available, which are not directly applicable for GNNs. Thus, we investigate a novel problem of learning a robust GNN with noisy and limited labels. To alleviate the negative effects of label noise, we propose to link the unlabeled nodes with labeled nodes of high feature similarity to bring more clean label information. Furthermore, accurate pseudo labels could be obtained by this strategy to provide more supervision and further reduce the effects of label noise. Our theoretical and empirical analysis verify the effectiveness of these two strategies under mild conditions. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed method in learning a robust GNN with noisy and limited labels.

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Index Terms

NRGNN: Learning a Label Noise Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs
1. Computing methodologies
  1. Machine learning
    1. Learning settings
      1. Semi-supervised learning settings
    2. Machine learning approaches
      1. Neural networks

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cover image ACM Conferences

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

August 2021

4259 pages

ISBN:9781450383325

DOI:10.1145/3447548

General Chairs:
Feida Zhu
Singapore Management University
,
Beng Chin Ooi
National University of Singapore
,
Chunyan Miao
Nanyang Technology University
,
Program Chairs:
Haixun Wang,
Iryna Skrypnyk,
Wynne Hsu,
Sanjay Chawla

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Published: 14 August 2021

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Cited By

Cai XChen MWang CZhang H(2025)Motif-aware curriculum learning for node classificationNeural Networks10.1016/j.neunet.2024.107089184(107089)Online publication date: Apr-2025
https://doi.org/10.1016/j.neunet.2024.107089
Li DLi XGan ZLi QQu BWang J(2025)Rethinking the impact of noisy labels in graph classification: A utility and privacy perspectiveNeural Networks10.1016/j.neunet.2024.106919182(106919)Online publication date: Feb-2025
https://doi.org/10.1016/j.neunet.2024.106919
Yang YGuan SWen X(2025)Enhancing robustness in implicit feedback recommender systems with subgraph contrastive learningInformation Processing & Management10.1016/j.ipm.2024.10396262:1(103962)Online publication date: Jan-2025
https://doi.org/10.1016/j.ipm.2024.103962
Wu YHuang HSong YJin H(2025)Soft-GNN: towards robust graph neural networks via self-adaptive data utilizationFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-024-3575-519:4Online publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1007/s11704-024-3575-5
Guo ZWu ZXiao TAggarwal CLiu HWang S(2025)Counterfactual Learning on Graphs: A SurveyMachine Intelligence Research10.1007/s11633-024-1519-z22:1(17-59)Online publication date: 24-Jan-2025
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Wu YYao JXia XYu JWang RHan BLiu TSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Mitigating label noise on graphs via topological sample selectionProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694283(53944-53972)Online publication date: 21-Jul-2024
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Yin NShen LChen CHua XLuo X(2024)SPORT: A Subgraph Perspective on Graph Classification with Label NoiseACM Transactions on Knowledge Discovery from Data10.1145/368746818:9(1-20)Online publication date: 28-Aug-2024
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