mlsanityData to 7 Oct 2026

Task-Adaptive Few-shot Node Classification

arXiv 2206.11972 ↗First posted 23 Jun 2022cs.LGcited by 43

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Abstract (arXiv)

Node classification is of great importance among various graph mining tasks. In practice, real-world graphs generally follow the long-tail distribution, where a large number of classes only consist of limited labeled nodes. Although Graph Neural Networks (GNNs) have achieved significant improvements in node classification, their performance decreases substantially in such a few-shot scenario. The main reason can be attributed to the vast generalization gap between meta-training and meta-test due to the task variance caused by different node/class distributions in meta-tasks (i.e., node-level and class-level variance). Therefore, to effectively alleviate the impact of task variance, we propose a task-adaptive node classification framework under the few-shot learning setting. Specifically, we first accumulate meta-knowledge across classes with abundant labeled nodes. Then we transfer such knowledge to the classes with limited labeled nodes via our proposed task-adaptive modules. In particular, to accommodate the different node/class distributions among meta-tasks, we propose three essential modules to perform node-level, class-level, and task-level adaptations in each meta-task, respectively. In this way, our framework can conduct adaptations to different meta-tasks and thus advance the model generalization performance on meta-test tasks. Extensive experiments on four prevalent node classification datasets demonstrate the superiority of our framework over the state-of-the-art baselines. Our code is provided at https://github.com/SongW-SW/TENT.

Key facts. Cited by 43 papers in the map (data through 7 Oct 2026). Builds on 30 of its 56 references that are in the map. First posted on arXiv 23 Jun 2022.

Builds on

30 of 56 references.

References · 30

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
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  6. 2017Inductive Representation Learning on Large Graphscited by 6,284
  7. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  8. 2018How Powerful are Graph Neural Networks?cited by 4,355
  9. 2017Prototypical Networks for Few-shot Learningcited by 3,001
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18 more
  1. 2016Matching Networks for One Shot Learningcited by 2,246
  2. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  3. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  4. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,139
  5. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 875
  6. 2018TADAM: Task dependent adaptive metric for improved few-shot learningcited by 408
  7. 2018Deep Learning on Graphs: A Surveycited by 393
  8. 2017Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Rankingcited by 332
  9. 2019Meta-GNN: On Few-shot Node Classification in Graph Meta-learningcited by 105
  10. 2015Inferring Networks of Substitutable and Complementary Productscited by 91
  11. 2020Graph Prototypical Networks for Few-shot Learning on Attributed Networkscited by 90
  12. 2020Graph Meta Learning via Local Subgraphscited by 89
  13. 2018One-Shot Relational Learning for Knowledge Graphscited by 78
  14. 2019Graph Few-shot Learning via Knowledge Transfercited by 63
  15. 2020Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measurescited by 31
  16. 2020TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classificationcited by 30
  17. 2019Learning to Propagate for Graph Meta-Learningcited by 29
  18. 2021Meta-Inductive Node Classification across Graphscited by 16

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 15; 2024: 16; 2025: 5; 2026: 4.

43 papers.

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  1. 2023One for All: Towards Training One Graph Model for All Classification Taskscited by 239
  2. 2023PRODIGY: Enabling In-context Learning Over Graphscited by 97
  3. 2024GFT: Graph Foundation Model with Transferable Tree Vocabularycited by 82
  4. 2024UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphscited by 60
  5. 2024GOFA: A Generative One-For-All Model for Joint Graph Language Modelingcited by 56
  6. 2023Virtual Node Tuning for Few-shot Node Classificationcited by 46
  7. 2024Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Treescited by 30
  8. 2022Graph Few-shot Learning with Task-specific Structurescited by 26
  9. 2022Transductive Linear Probing: A Novel Framework for Few-Shot Node Classificationcited by 24
  10. 2023Leveraging Large Language Models for Node Generation in Few-Shot Learning on Text-Attributed Graphscited by 23
  11. 2023A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directionscited by 15
  12. 2024GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via Feature Alignmentcited by 15
31 more
  1. 2023Few-shot Node Classification with Extremely Weak Supervisioncited by 14
  2. 2023Contrastive Meta-Learning for Few-shot Node Classificationcited by 11
  3. 2024Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphscited by 11
  4. 2023Federated Few-shot Learningcited by 9
  5. 2023Task-Equivariant Graph Few-shot Learningcited by 8
  6. 2023Road Planning for Slums via Deep Reinforcement Learningcited by 7
  7. 2024Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmarkcited by 7
  8. 2024Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Trainingcited by 7
  9. 2025Enhancing Unsupervised Graph Few-shot Learning via Set Functions and Optimal Transportcited by 6
  10. 2024A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learningcited by 5
  11. 2025Dual-level Mixup for Graph Few-shot Learning with Fewer Taskscited by 5
  12. 2025Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigationcited by 5
  13. 2024Fast Graph Sharpness-Aware Minimization for Enhancing and Accelerating Few-Shot Node Classificationcited by 4
  14. 2023Inductive Linear Probing for Few-shot Node Classificationcited by 3
  15. 2023Cross-heterogeneity Graph Few-shot Learningcited by 3
  16. 2023Graph Contrastive Learning Meets Graph Meta Learning: A Unified Method for Few-shot Node Taskscited by 3
  17. 2025Graph Few-Shot Learning via Adaptive Spectrum Experts and Cross-Set Distribution Calibrationcited by 3
  18. 2023Fair Few-shot Learning with Auxiliary Setscited by 2
  19. 2024Hound: Hunting Supervision Signals for Few and Zero Shot Node Classification on Text-attributed Graphcited by 2
  20. 2024Inductive Graph Few-shot Class Incremental Learningcited by 2
  21. 2022Contrastive Graph Few-Shot Learningcited by 1
  22. 2023Unsupervised Episode Generation for Graph Meta-learningcited by 1
  23. 2024GraphRCG: Self-Conditioned Graph Generationcited by 1
  24. 2024Graph Mining under Data scarcitycited by 1
  25. 2025Parameter-Free Hypergraph Neural Network for Few-Shot Node Classificationcited by 1
  26. 2026Advancing Graph Few-Shot Learning via In-Context Learningcited by 1
  27. 2024CPT: Competence-progressive Training Strategy for Few-shot Node Classificationcited by 0
  28. 2024Virtual Node Generation for Node Classification in Sparsely-Labeled Graphscited by 0
  29. 2026Mochi: Aligning Pre-training and Inference for Efficient Graph Foundation Models via Meta-Learningcited by 0
  30. 2026Improving Graph Few-shot Learning with Hyperbolic Space and Denoising Diffusioncited by 0
  31. 2026Chimaera: A Mixture-of-Graph-Experts Architecture for Cross-Task and Cross-Dataset Graph Learningcited by 0