research-article

Towards Expansive and Adaptive Hard Negative Mining: Graph Contrastive Learning via Subspace Preserving

Authors:

Feiping NieAuthors Info & Claims

WWW '24: Proceedings of the ACM Web Conference 2024

Pages 322 - 333

https://doi.org/10.1145/3589334.3645327

Published: 13 May 2024 Publication History

Abstract

Graph Neural Networks (GNNs) have emerged as the predominant approach for analyzing graph data on the web and beyond. Contrastive learning (CL), a self-supervised paradigm, not only mitigates reliance on annotations but also has potential in performance. The hard negative sampling strategy that benefits CL in other domains proves ineffective in the context of Graph Contrastive Learning (GCL) due to the message passing mechanism. Embracing the subspace hypothesis in clustering, we propose a method towards expansive and adaptive hard negative mining, referred to as G raph contR astive leA rning via subsP ace prE serving (GRAPE ). Beyond homophily, we argue that false negatives are prevalent over an expansive range and exploring them confers benefits upon GCL. Diverging from existing neighbor-based methods, our method seeks to mine long-range hard negatives throughout subspace, where message passing is conceived as interactions between subspaces. %Empirical investigations back up this strategy. Additionally, our method adaptively scales the hard negatives set through subspace preservation during training. In practice, we develop two schemes to enhance GCL that are pluggable into existing GCL frameworks. The underlying mechanisms are analyzed and the connections to related methods are investigated. Comprehensive experiments demonstrate that our method outperforms across diverse graph datasets and remains competitive across varied application scenarios\footnoteOur code is available at https://github.com/zz-haooo/WWW24-GRAPE. .

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https://doi.org/10.3390/rs16214084
Guan RTu WLi ZYu HHu DChen YTang CYuan QLiu X(2024)Spatial–Spectral Graph Contrastive Clustering With Hard Sample Mining for Hyperspectral ImagesIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.346464862(1-16)Online publication date: 2024
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Index Terms

Towards Expansive and Adaptive Hard Negative Mining: Graph Contrastive Learning via Subspace Preserving

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cover image ACM Conferences

WWW '24: Proceedings of the ACM Web Conference 2024

May 2024

4826 pages

ISBN:9798400701719

DOI:10.1145/3589334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Chong-Wah Ngo
Singapore Management University
,
Proceedings Chair:
Roy Ka-Wei Lee
Singapore University of Technology and Design
,
Program Chairs:
Ravi Kumar
Google
,
Hady W. Lauw
Singapore Management University

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Cited By

Zhang JChen S(2025)Topology reorganized graph contrastive learning with mitigating semantic driftPattern Recognition10.1016/j.patcog.2024.111160159(111160)Online publication date: Mar-2025
https://doi.org/10.1016/j.patcog.2024.111160
He ZLi GWang ZHe GYan HWang R(2024)Deep Ensemble Remote Sensing Scene Classification via Category Distribution AssociationRemote Sensing10.3390/rs1621408416:21(4084)Online publication date: 1-Nov-2024
https://doi.org/10.3390/rs16214084
Guan RTu WLi ZYu HHu DChen YTang CYuan QLiu X(2024)Spatial–Spectral Graph Contrastive Clustering With Hard Sample Mining for Hyperspectral ImagesIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.346464862(1-16)Online publication date: 2024
https://doi.org/10.1109/TGRS.2024.3464648

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