mlsanityData to 14 Oct 2026

Graph Few-shot Learning via Knowledge Transfer

arXiv 1910.03053 ↗First posted 7 Oct 2019cs.LG stat.MLcited by 63

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Abstract (arXiv)

Towards the challenging problem of semi-supervised node classification, there have been extensive studies. As a frontier, Graph Neural Networks (GNNs) have aroused great interest recently, which update the representation of each node by aggregating information of its neighbors. However, most GNNs have shallow layers with a limited receptive field and may not achieve satisfactory performance especially when the number of labeled nodes is quite small. To address this challenge, we innovatively propose a graph few-shot learning (GFL) algorithm that incorporates prior knowledge learned from auxiliary graphs to improve classification accuracy on the target graph. Specifically, a transferable metric space characterized by a node embedding and a graph-specific prototype embedding function is shared between auxiliary graphs and the target, facilitating the transfer of structural knowledge. Extensive experiments and ablation studies on four real-world graph datasets demonstrate the effectiveness of our proposed model.

Key facts. Cited by 63 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Oct 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 12; 2022: 15; 2023: 14; 2024: 10; 2025: 8; 2026: 4.

63 papers.

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  1. 2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 163
  2. 2021Few-Shot Graph Learning for Molecular Property Predictioncited by 81
  3. 2023Virtual Node Tuning for Few-shot Node Classificationcited by 46
  4. 2022Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfercited by 43
  5. 2022Task-Adaptive Few-shot Node Classificationcited by 43
  6. 2021Hierarchical Prototype Networks for Continual Graph Representation Learningcited by 28
  7. 2021Meta Propagation Networks for Graph Few-shot Semi-supervised Learningcited by 25
  8. 2022FAITH: Few-Shot Graph Classification with Hierarchical Task Graphscited by 22
  9. 2022Geometer: Graph Few-Shot Class-Incremental Learning via Prototype Representationcited by 22
  10. 2024Continual Learning on Graphs: Challenges, Solutions, and Opportunitiescited by 21
  11. 2022Cross-Domain Few-Shot Graph Classificationcited by 19
  12. 2024A Survey of Data-Efficient Graph Learningcited by 18
38 more
  1. 2021Meta-Inductive Node Classification across Graphscited by 16
  2. 2023Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networkscited by 16
  3. 2023A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directionscited by 15
  4. 2024Topology-aware Embedding Memory for Continual Learning on Expanding Networkscited by 14
  5. 2023Learning to Count Isomorphisms with Graph Neural Networkscited by 13
  6. 2022Few-Shot Learning on Graphscited by 11
  7. 2022Toward Robust Graph Semi-Supervised Learning against Extreme Data Scarcitycited by 11
  8. 2024Node-Time Conditional Prompt Learning In Dynamic Graphscited by 11
  9. 2024Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphscited by 11
  10. 2021What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Datacited by 10
  11. 2021Node-wise Localization of Graph Neural Networkscited by 10
  12. 2021A Lightweight and Accurate Spatial-Temporal Transformer for Traffic Forecastingcited by 9
  13. 2022Supervised Graph Contrastive Learning for Few-shot Node Classificationcited by 9
  14. 2022Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfercited by 8
  15. 2021Meta-Learning with Graph Neural Networks: Methods and Applicationscited by 7
  16. 2023Edge-free but Structure-aware: Prototype-Guided Knowledge Distillation from GNNs to MLPscited by 7
  17. 2024Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Trainingcited by 7
  18. 2023A Survey On Few-shot Knowledge Graph Completion with Structural and Commonsense Knowledgecited by 6
  19. 2023Graph-based Knowledge Distillation: A survey and experimental evaluationcited by 6
  20. 2024A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learningcited by 6
  21. 2022Graph Representation Learning for Multi-Task Settings: a Meta-Learning Approachcited by 5
  22. 2022When Facial Expression Recognition Meets Few-Shot Learning: A Joint and Alternate Learning Frameworkcited by 5
  23. 2025Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigationcited by 5
  24. 2021Structure-Enhanced Meta-Learning For Few-Shot Graph Classificationcited by 4
  25. 2022Metric Based Few-Shot Graph Classificationcited by 4
  26. 2022Few-Shot Semantic Relation Prediction across Heterogeneous Graphscited by 4
  27. 2024MLDGG: Meta-Learning for Domain Generalization on Graphscited by 4
  28. 2023Graph Contrastive Learning Meets Graph Meta Learning: A Unified Method for Few-shot Node Taskscited by 3
  29. 2021Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classificationcited by 2
  30. 2021Human-In-The-Loop Document Layout Analysiscited by 2
  31. 2025GMoPE:A Prompt-Expert Mixture Framework for Graph Foundation Modelscited by 2
  32. 2026Data-centric Prompt Tuning for Dynamic Graphscited by 2
  33. 2022Contrastive Graph Few-Shot Learningcited by 1
  34. 2023MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detectioncited by 1
  35. 2023Dynamic Link Prediction for New Nodes in Temporal Graph Networkscited by 1
  36. 2023STDA-Meta: A Meta-Learning Framework for Few-Shot Traffic Predictioncited by 1
  37. 2024Graph Mining under Data scarcitycited by 1
  38. 2025TMetaNet: Topological Meta-Learning Framework for Dynamic Link Predictioncited by 1

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