research-article

ALEX: Towards Effective Graph Transfer Learning with Noisy Labels

Authors:

Ming ZhangAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 3647 - 3656

https://doi.org/10.1145/3581783.3612026

Published: 27 October 2023 Publication History

Abstract

Graph Neural Networks (GNNs) have garnered considerable interest due to their exceptional performance in a wide range of graph machine learning tasks. Nevertheless, the majority of GNN-based approaches have been examined using well-annotated benchmark datasets, leading to suboptimal performance in real-world graph learning scenarios. To bridge this gap, the present paper investigates the problem of graph transfer learning in the presence of label noise, which transfers knowledge from a noisy source graph to an unlabeled target graph. We introduce a novel technique termed Balance Alignment and Information-aware Examination (ALEX) to address this challenge. ALEX first employs singular value decomposition to generate different views with crucial structural semantics, which help provide robust node representations using graph contrastive learning. To mitigate both label shift and domain shift, we estimate a prior distribution to build subgraphs with balanced label distributions. Building on this foundation, an adversarial domain discriminator is incorporated for the implicit domain alignment of complex multi-modal distributions. Furthermore, we project node representations into a different space, optimizing the mutual information between the projected features and labels. Subsequently, the inconsistency of similarity structures is evaluated to identify noisy samples with potential overfitting. Comprehensive experiments on various benchmark datasets substantiate the outstanding superiority of the proposed ALEX in different settings.

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Cited By

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
Ju WFang ZGu YLiu ZLong QQiao ZQin YShen JSun FXiao ZYang JYuan JZhao YWang YLuo XZhang M(2024)A Comprehensive Survey on Deep Graph Representation LearningNeural Networks10.1016/j.neunet.2024.106207173:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.neunet.2024.106207
Tian YNiu XChai J(2024)Partial label learning via weighted centroid clustering disambiguationNeurocomputing10.1016/j.neucom.2024.128312604(128312)Online publication date: Nov-2024
https://doi.org/10.1016/j.neucom.2024.128312
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Index Terms

ALEX: Towards Effective Graph Transfer Learning with Noisy Labels
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms

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cover image ACM Conferences

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
,
Tao Mei
HiDream.ai, China
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Program Chairs:
Marco Bertini
University of Florence, Italy
,
Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
,
Pradeep K. Atrey
University at Albany, State University of New York, USA
,
M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

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Published: 27 October 2023

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MM '23: The 31st ACM International Conference on Multimedia

October 29 - November 3, 2023

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Cited By

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
Ju WFang ZGu YLiu ZLong QQiao ZQin YShen JSun FXiao ZYang JYuan JZhao YWang YLuo XZhang M(2024)A Comprehensive Survey on Deep Graph Representation LearningNeural Networks10.1016/j.neunet.2024.106207173:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.neunet.2024.106207
Tian YNiu XChai J(2024)Partial label learning via weighted centroid clustering disambiguationNeurocomputing10.1016/j.neucom.2024.128312604(128312)Online publication date: Nov-2024
https://doi.org/10.1016/j.neucom.2024.128312
Ghayekhloo MNickabadi A(2024)Supervised contrastive learning for graph representation enhancementNeurocomputing10.1016/j.neucom.2024.127710588:COnline publication date: 17-Jul-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.127710
Lu ZLiu YWen GZhou BZhang WZhang J(2024)Noise-resistant graph neural networks with manifold consistency and label consistencyExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.123120245:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.123120

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