mlsanityData to 7 Oct 2026

Few-Shot Learning on Graphs

arXiv 2203.09308 ↗First posted 17 Mar 2022cs.LGcited by 11

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Abstract (arXiv)

Graph representation learning has attracted tremendous attention due to its remarkable performance in many real-world applications. However, prevailing supervised graph representation learning models for specific tasks often suffer from label sparsity issue as data labeling is always time and resource consuming. In light of this, few-shot learning on graphs (FSLG), which combines the strengths of graph representation learning and few-shot learning together, has been proposed to tackle the performance degradation in face of limited annotated data challenge. There have been many studies working on FSLG recently. In this paper, we comprehensively survey these work in the form of a series of methods and applications. Specifically, we first introduce FSLG challenges and bases, then categorize and summarize existing work of FSLG in terms of three major graph mining tasks at different granularity levels, i.e., node, edge, and graph. Finally, we share our thoughts on some future research directions of FSLG. The authors of this survey have contributed significantly to the AI literature on FSLG over the last few years.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 50 of its 76 references that are in the map. First posted on arXiv 17 Mar 2022.

Builds on

50 of 76 references.

References · 50

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  2. 2017Graph Attention Networkscited by 8,854
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  11. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,139
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38 more
  1. 2020Meta-Learning in Neural Networks: A Surveycited by 944
  2. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 786
  3. 2017Convolutional 2D Knowledge Graph Embeddingscited by 729
  4. 2019Graph Neural Networks for Social Recommendationcited by 711
  5. 2019Generalizing from a Few Examples: A Survey on Few-Shot Learningcited by 462
  6. 2018Deep Learning on Graphs: A Surveycited by 393
  7. 2018Probabilistic Model-Agnostic Meta-Learningcited by 254
  8. 2017One-Shot Imitation Learningcited by 232
  9. 2020Few-Shot Learning via Learning the Representation, Provablycited by 213
  10. 2020Provable Meta-Learning of Linear Representationscited by 161
  11. 2019Meta-GNN: On Few-shot Node Classification in Graph Meta-learningcited by 105
  12. 2017Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Networkcited by 102
  13. 2020Graph Prototypical Networks for Few-shot Learning on Attributed Networkscited by 90
  14. 2020Graph Meta Learning via Local Subgraphscited by 89
  15. 2021Few-Shot Graph Learning for Molecular Property Predictioncited by 81
  16. 2018One-Shot Relational Learning for Knowledge Graphscited by 78
  17. 2019Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphscited by 73
  18. 2021Few-shot Network Anomaly Detection via Cross-network Meta-learningcited by 64
  19. 2019Graph Few-shot Learning via Knowledge Transfercited by 63
  20. 2019Few-Shot Knowledge Graph Completioncited by 63
  21. 2018Diverse Few-Shot Text Classification with Multiple Metricscited by 61
  22. 2020ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Predictioncited by 58
  23. 2020Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Predictioncited by 46
  24. 2020Adaptive Attentional Network for Few-Shot Knowledge Graph Completioncited by 42
  25. 2021Property-Aware Relation Networks for Few-Shot Molecular Property Predictioncited by 41
  26. 2021Graph Few-shot Class-incremental Learningcited by 40
  27. 2019A Theoretical Analysis of the Number of Shots in Few-Shot Learningcited by 39
  28. 2021Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completioncited by 32
  29. 2020Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measurescited by 31
  30. 2019Few-Shot Representation Learning for Out-Of-Vocabulary Wordscited by 30
  31. 2019Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relationscited by 30
  32. 2020Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphscited by 30
  33. 2021Meta Propagation Networks for Graph Few-shot Semi-supervised Learningcited by 25
  34. 2019Meta-Graph: Few Shot Link Prediction via Meta Learningcited by 24
  35. 2020Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embeddingcited by 19
  36. 2022Cross-Domain Few-Shot Graph Classificationcited by 19
  37. 2020One-shot Learning for Temporal Knowledge Graphscited by 14
  38. 2021Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphscited by 8

Built on by

Cited by year

Citing papers per year: 2022: 5; 2023: 3; 2024: 3.

11 papers.

  1. 2022Transductive Linear Probing: A Novel Framework for Few-Shot Node Classificationcited by 24
  2. 2023Graph Anomaly Detection in Time Series: A Surveycited by 18
  3. 2023Curriculum Graph Machine Learning: A Surveycited by 15
  4. 2022Supervised Graph Contrastive Learning for Few-shot Node Classificationcited by 9
  5. 2022Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparisoncited by 8
  6. 2024Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphscited by 6
  7. 2022Few-Shot Semantic Relation Prediction across Heterogeneous Graphscited by 4
  8. 2024Towards Few-shot Self-explaining Graph Neural Networkscited by 4
  9. 2023Cross-heterogeneity Graph Few-shot Learningcited by 3
  10. 2022A Survey of Learning on Small Data: Generalization, Optimization, and Challengecited by 1
  11. 2024Few-shot Learning on Heterogeneous Graphs: Challenges, Progress, and Prospectscited by 0