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RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification

Authors:

Ming ZhangAuthors Info & Claims

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

Pages 3817 - 3826

https://doi.org/10.1145/3581783.3612360

Published: 27 October 2023 Publication History

Abstract

Graph classification is a crucial task in many real-world multimedia applications, where graphs can represent various multimedia data types such as images, videos, and social networks. Previous efforts have applied graph neural networks (GNNs) in balanced situations where the class distribution is balanced. However, real-world data typically exhibit long-tailed class distributions, resulting in a bias towards the head classes when using GNNs and limited generalization ability over the tail classes. Recent approaches mainly focus on re-balancing different classes during model training, which fails to explicitly introduce new knowledge and sacrifices the performance of the head classes. To address these drawbacks, we propose a novel framework called Retrieval Augmented Hybrid Network (RAHNet) to jointly learn a robust feature extractor and an unbiased classifier in a decoupled manner. In the feature extractor training stage, we develop a graph retrieval module to search for relevant graphs that directly enrich the intra-class diversity for the tail classes. Moreover, we innovatively optimize a category-centered supervised contrastive loss to obtain discriminative representations, which is more suitable for long-tailed scenarios. In the classifier fine-tuning stage, we balance the classifier weights with two weight regularization techniques, i.e., Max-norm and weight decay. Experiments on various popular benchmarks verify the superiority of the proposed method against state-of-the-art approaches.

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Cited By

Zeng AWang LZhang WLin X(2024)Efficient and Effective Augmentation Framework With Latent Mixup and Label-Guided Contrastive Learning for Graph ClassificationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.347165936:12(8066-8078)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3471659
Che FTao J(2024)M2ixKGNeural Networks10.1016/j.neunet.2024.106358177:COnline publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1016/j.neunet.2024.106358
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
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Index Terms

RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Data mining

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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

Ottawa ON, Canada

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Cited By

Zeng AWang LZhang WLin X(2024)Efficient and Effective Augmentation Framework With Latent Mixup and Label-Guided Contrastive Learning for Graph ClassificationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.347165936:12(8066-8078)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3471659
Che FTao J(2024)M2ixKGNeural Networks10.1016/j.neunet.2024.106358177:COnline publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1016/j.neunet.2024.106358
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
Zhang BKyutoku HDoman KKomamizu TIde IQian J(2024)Cross-modal recipe retrieval based on unified text encoder with fine-grained contrastive learningKnowledge-Based Systems10.1016/j.knosys.2024.112641305(112641)Online publication date: Dec-2024
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Zeng LFeng ZChen JWang S(2024)Long-tailed visual classification based on supervised contrastive learning with multi-view fusionKnowledge-Based Systems10.1016/j.knosys.2024.112301301(112301)Online publication date: Oct-2024
https://doi.org/10.1016/j.knosys.2024.112301
Li ZYao TWang LLi YWang G(2024)Supervised Contrastive Discrete Hashing for cross-modal retrievalKnowledge-Based Systems10.1016/j.knosys.2024.111837295:COnline publication date: 18-Jul-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111837
Bian JXie XLai JNie F(2024)Multi-view contrastive clustering via integrating graph aggregation and confidence enhancementInformation Fusion10.1016/j.inffus.2024.102393108:COnline publication date: 17-Jul-2024
https://dl.acm.org/doi/10.1016/j.inffus.2024.102393
Suryawanshi YShah VRandar SJoshi A(2024)Audio meets text: a loss-enhanced journey with manifold mixup and re-rankingKnowledge and Information Systems10.1007/s10115-024-02283-4Online publication date: 19-Nov-2024
https://doi.org/10.1007/s10115-024-02283-4

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