mlsanityData to 7 Oct 2026

Cross-Domain Few-Shot Graph Classification

arXiv 2201.08265 ↗First posted 20 Jan 2022cs.LG cs.NEcited by 19

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Abstract (arXiv)

We study the problem of few-shot graph classification across domains with nonequivalent feature spaces by introducing three new cross-domain benchmarks constructed from publicly available datasets. We also propose an attention-based graph encoder that uses three congruent views of graphs, one contextual and two topological views, to learn representations of task-specific information for fast adaptation, and task-agnostic information for knowledge transfer. We run exhaustive experiments to evaluate the performance of contrastive and meta-learning strategies. We show that when coupled with metric-based meta-learning frameworks, the proposed encoder achieves the best average meta-test classification accuracy across all benchmarks. The source code and data will be released here: https://github.com/kavehhassani/metagrl

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 50 of its 61 references that are in the map. First posted on arXiv 20 Jan 2022.

Builds on

50 of 61 references.

References · 50

  1. 2017Attention Is All You Needcited by 59,932
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  6. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
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38 more
  1. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,139
  2. 2017CyCADA: Cycle-Consistent Adversarial Domain Adaptationcited by 1,079
  3. 2019Strategies for Pre-training Graph Neural Networkscited by 964
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  9. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
  10. 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 718
  11. 2019A Closer Look at Few-shot Classificationcited by 679
  12. 2020Contrastive Multi-View Representation Learning on Graphscited by 651
  13. 2020GCC: Graph Contrastive Coding for Graph Neural Network Pre-Trainingcited by 461
  14. 2018Generalizing to Unseen Domains via Adversarial Data Augmentationcited by 451
  15. 2018Domain Adaptive Faster R-CNN for Object Detection in the Wildcited by 442
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  19. 2018Deep Learning on Graphs: A Surveycited by 393
  20. 2017Meta-SGD: Learning to Learn Quickly for Few-Shot Learningcited by 377
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  22. 2019Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAMLcited by 359
  23. 2017Few-Shot Learning with Graph Neural Networkscited by 324
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  25. 2020Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformationcited by 183
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  34. 2020Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measurescited by 31
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 4; 2024: 3; 2025: 5; 2026: 2.

19 papers.

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  1. 2025SAMGPT: Text-free Graph Foundation Model for Multi-domain Pre-training and Cross-domain Adaptationcited by 61
  2. 2023CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classificationcited by 46
  3. 2025Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignmentcited by 41
  4. 2023A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directionscited by 15
  5. 2022Learning Graph Augmentations to Learn Graph Representationscited by 14
  6. 2023Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Surveycited by 14
  7. 2022Few-Shot Learning on Graphscited by 11
  8. 2023Graph Out-of-Distribution Generalization with Controllable Data Augmentationcited by 9
  9. 2022Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacycited by 7
  10. 2022Evaluating Graph Generative Models with Contrastively Learned Featurescited by 5
  11. 2024Towards Few-Shot Learning in the Open World: A Review and Beyondcited by 5
  12. 2022Metric Based Few-Shot Graph Classificationcited by 4
7 more
  1. 2024MLDGG: Meta-Learning for Domain Generalization on Graphscited by 4
  2. 2025Towards Effective, Stealthy, and Persistent Backdoor Attacks Targeting Graph Foundation Modelscited by 3
  3. 2024Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural Networkscited by 2
  4. 2025SOLA-GCL: Subgraph-Oriented Learnable Augmentation Method for Graph Contrastive Learningcited by 2
  5. 2025A Survey of Cross-domain Graph Learning: Progress and Future Directionscited by 2
  6. 2026Node4All: Learning Node Representation Beyond Datasetscited by 2
  7. 2026GraphVec: Cross-Domain Graph Vectorization for Graph-Level Representation Learningcited by 0