mlsanityData to 14 Oct 2026

Data Augmentation on Graphs: A Technical Survey

arXiv 2212.09970 ↗First posted 20 Dec 2022cs.LGcited by 10

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Abstract (arXiv)

In recent years, graph representation learning has achieved remarkable success while suffering from low-quality data problems. As a mature technology to improve data quality in computer vision, data augmentation has also attracted increasing attention in graph domain. To advance research in this emerging direction, this survey provides a comprehensive review and summary of existing graph data augmentation (GDAug) techniques. Specifically, this survey first provides an overview of various feasible taxonomies and categorizes existing GDAug studies based on multi-scale graph elements. Subsequently, for each type of GDAug technique, this survey formalizes standardized technical definition, discuss the technical details, and provide schematic illustration. The survey also reviews domain-specific graph data augmentation techniques, including those for heterogeneous graphs, temporal graphs, spatio-temporal graphs, and hypergraphs. In addition, this survey provides a summary of available evaluation metrics and design guidelines for graph data augmentation. Lastly, it outlines the applications of GDAug at both the data and model levels, discusses open issues in the field, and looks forward to future directions. The latest advances in GDAug are summarized in GitHub.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 79 of its 145 references that are in the map. First posted on arXiv 20 Dec 2022.

Builds on

79 of 145 references.

References · 79

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Inductive Representation Learning on Large Graphscited by 6,320
  3. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  4. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,884
  5. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
  6. 2019Self-training with Noisy Student improves ImageNet classificationcited by 1,311
  7. 2020Graph Contrastive Learning with Augmentationscited by 1,208
  8. 2018Deep Graph Infomaxcited by 1,106
  9. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,102
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  11. 2019DropEdge: Towards Deep Graph Convolutional Networks on Node Classificationcited by 680
  12. 2020Contrastive Multi-View Representation Learning on Graphscited by 652
38 more
  1. 2019Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological Viewcited by 545
  2. 2020Deep Graph Contrastive Representation Learningcited by 539
  3. 2019DeepGCNs: Can GCNs Go as Deep as CNNs?cited by 527
  4. 2020Graph Contrastive Learning with Adaptive Augmentationcited by 519
  5. 2020GCC: Graph Contrastive Coding for Graph Neural Network Pre-Trainingcited by 463
  6. 2019InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximizationcited by 441
  7. 2019Diffusion Improves Graph Learningcited by 414
  8. 2021A Survey of Data Augmentation Approaches for NLPcited by 409
  9. 2020Self-supervised Graph Learning for Recommendationcited by 305
  10. 2021Graph Self-Supervised Learning: A Surveycited by 289
  11. 2021Graph Contrastive Learning Automatedcited by 287
  12. 2020Graph Random Neural Network for Semi-Supervised Learning on Graphscited by 213
  13. 2020Data Augmentation for Graph Neural Networkscited by 203
  14. 2021Adversarial Graph Augmentation to Improve Graph Contrastive Learningcited by 200
  15. 2020How Does Mixup Help With Robustness and Generalization?cited by 193
  16. 2021Large-Scale Representation Learning on Graphs via Bootstrappingcited by 186
  17. 2021Self-Supervised Learning of Graph Neural Networks: A Unified Reviewcited by 184
  18. 2021GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networkscited by 147
  19. 2022G-Mixup: Graph Data Augmentation for Graph Classificationcited by 146
  20. 2021From Canonical Correlation Analysis to Self-supervised Graph Neural Networkscited by 141
  21. 2021Self-supervised Learning on Graphs: Contrastive, Generative,or Predictivecited by 136
  22. 2022Data Augmentation for Deep Graph Learning: A Surveycited by 133
  23. 2021Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learningcited by 130
  24. 2020Learning to Drop: Robust Graph Neural Network via Topological Denoisingcited by 127
  25. 2021An Empirical Study of Graph Contrastive Learningcited by 113
  26. 2019Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labelscited by 111
  27. 2020Transfer Learning of Graph Neural Networks with Ego-graph Information Maximizationcited by 93
  28. 2020Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learningcited by 90
  29. 2021Graph Barlow Twins: A self-supervised representation learning framework for graphscited by 85
  30. 2021HDMI: High-order Deep Multiplex Infomaxcited by 82
  31. 2022Spatio-Temporal Self-Supervised Learning for Traffic Flow Predictioncited by 77
  32. 2021SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanismcited by 75
  33. 2021NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphscited by 73
  34. 2021Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learningcited by 70
  35. 2022Graph Rationalization with Environment-based Augmentationscited by 70
  36. 2020Contrastive Self-supervised Learning for Graph Classificationcited by 68
  37. 2021FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learningcited by 68
  38. 2021MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular Graphcited by 65

50 most cited of 79 All 79 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 2; 2026: 5.

10 papers.

  1. 2023Clarify Confused Nodes via Separated Learningcited by 5
  2. 2024Superpixel Graph Contrastive Clustering with Semantic-Invariant Augmentations for Hyperspectral Imagescited by 3
  3. 2024EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learningcited by 2
  4. 2025AEGIS: Authentic Edge Growth In Sparsity for Link Prediction in Edge-Sparse Bipartite Knowledge Graphscited by 0
  5. 2025Understanding InfoNCE: Transition Probability Matrix Induced Feature Clusteringcited by 0
  6. 2026Simple Network Graph Comparative Learningcited by 0
  7. 2026Detecting Miscitation on the Scholarly Web through LLM-Augmented Text-Rich Graph Learningcited by 0
  8. 2026CrossHGL: A Text-Free Foundation Model for Cross-Domain Heterogeneous Graph Learningcited by 0
  9. 2026Geometric Self-Supervised Pre-training for Neural Combinatorial Optimizationcited by 0
  10. 2026GraphK: Variable-Size Graph Generation with Efficient Edge Constructioncited by 0