mlsanityData to 7 Oct 2026

Supervised Graph Contrastive Learning for Few-shot Node Classification

arXiv 2203.15936 ↗First posted 29 Mar 2022cs.LGcited by 9

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Abstract (arXiv)

Graphs are present in many real-world applications, such as financial fraud detection, commercial recommendation, and social network analysis. But given the high cost of graph annotation or labeling, we face a severe graph label-scarcity problem, i.e., a graph might have a few labeled nodes. One example of such a problem is the so-called textitfew-shot node classification. A predominant approach to this problem resorts to textitepisodic meta-learning. In this work, we challenge the status quo by asking a fundamental question whether meta-learning is a must for few-shot node classification tasks. We propose a new and simple framework under the standard few-shot node classification setting as an alternative to meta-learning to learn an effective graph encoder. The framework consists of supervised graph contrastive learning with novel mechanisms for data augmentation, subgraph encoding, and multi-scale contrast on graphs. Extensive experiments on three benchmark datasets (CoraFull, Reddit, Ogbn) show that the new framework significantly outperforms state-of-the-art meta-learning based methods.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 37 of its 43 references that are in the map. First posted on arXiv 29 Mar 2022.

Builds on

37 of 43 references.

References · 37

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  5. 2017Inductive Representation Learning on Large Graphscited by 6,284
  6. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  7. 2018How Powerful are Graph Neural Networks?cited by 4,355
  8. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  9. 2020Supervised Contrastive Learningcited by 2,803
  10. 2016Matching Networks for One Shot Learningcited by 2,246
  11. 2020Graph Contrastive Learning with Augmentationscited by 1,203
  12. 2019Strategies for Pre-training Graph Neural Networkscited by 964
25 more
  1. 2019Learning Representations by Maximizing Mutual Information Across Viewscited by 733
  2. 2020Contrastive Multi-View Representation Learning on Graphscited by 651
  3. 2020Graph Contrastive Learning with Adaptive Augmentationcited by 519
  4. 2020Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?cited by 435
  5. 2017Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Rankingcited by 332
  6. 2019A Baseline for Few-Shot Image Classificationcited by 298
  7. 2021Graph Contrastive Learning Automatedcited by 287
  8. 2020Supervised Contrastive Learning for Pre-trained Language Model Fine-tuningcited by 275
  9. 2021Adversarial Graph Augmentation to Improve Graph Contrastive Learningcited by 200
  10. 2022Data Augmentation for Deep Graph Learning: A Surveycited by 132
  11. 2019Meta-GNN: On Few-shot Node Classification in Graph Meta-learningcited by 105
  12. 2020Graph Prototypical Networks for Few-shot Learning on Attributed Networkscited by 90
  13. 2020Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learningcited by 90
  14. 2020Graph Meta Learning via Local Subgraphscited by 89
  15. 2021Few-Shot Graph Learning for Molecular Property Predictioncited by 81
  16. 2021Few-shot Network Anomaly Detection via Cross-network Meta-learningcited by 64
  17. 2019Graph Few-shot Learning via Knowledge Transfercited by 63
  18. 2021CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understandingcited by 50
  19. 2019Estimating Node Importance in Knowledge Graphs Using Graph Neural Networkscited by 43
  20. 2021Graph Few-shot Class-incremental Learningcited by 40
  21. 2022Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentationscited by 28
  22. 2021Meta Propagation Networks for Graph Few-shot Semi-supervised Learningcited by 25
  23. 2020Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embeddingcited by 19
  24. 2021Meta-Inductive Node Classification across Graphscited by 16
  25. 2022Few-Shot Learning on Graphscited by 11

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 6; 2025: 1.

9 papers.

  1. 2024From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judgecited by 480
  2. 2022Transductive Linear Probing: A Novel Framework for Few-Shot Node Classificationcited by 24
  3. 2023A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directionscited by 15
  4. 2024Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Trainingcited by 7
  5. 2024Inductive Graph Few-shot Class Incremental Learningcited by 2
  6. 2024BrainMAP: Learning Multiple Activation Pathways in Brain Networkscited by 2
  7. 2025Generative Risk Minimization for Out-of-Distribution Generalization on Graphscited by 2
  8. 2024Contrastive General Graph Matching with Adaptive Augmentation Samplingcited by 0
  9. 2024Virtual Node Generation for Node Classification in Sparsely-Labeled Graphscited by 0