mlsanityData to 7 Oct 2026

Graph Data Augmentation for Graph Machine Learning: A Survey

arXiv 2202.08871 ↗First posted 17 Feb 2022cs.LGcited by 61

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Abstract (arXiv)

Data augmentation has recently seen increased interest in graph machine learning given its demonstrated ability to improve model performance and generalization by added training data. Despite this recent surge, the area is still relatively under-explored, due to the challenges brought by complex, non-Euclidean structure of graph data, which limits the direct analogizing of traditional augmentation operations on other types of image, video or text data. Our work aims to give a necessary and timely overview of existing graph data augmentation methods; notably, we present a comprehensive and systematic survey of graph data augmentation approaches, summarizing the literature in a structured manner. We first introduce three different taxonomies for categorizing graph data augmentation methods from the data, task, and learning perspectives, respectively. Next, we introduce recent advances in graph data augmentation, differentiated by their methodologies and applications. We conclude by outlining currently unsolved challenges and directions for future research. Overall, our work aims to clarify the landscape of existing literature in graph data augmentation and motivates additional work in this area, providing a helpful resource for researchers and practitioners in the broader graph machine learning domain. Additionally, we provide a continuously updated reading list at https://github.com/zhao-tong/graph-data-augmentation-papers.

Key facts. Cited by 61 papers in the map (data through 7 Oct 2026). Builds on 99 of its 171 references that are in the map. First posted on arXiv 17 Feb 2022.

Builds on

99 of 171 references.

References · 99

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  5. 2017Inductive Representation Learning on Large Graphscited by 6,284
  6. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  7. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,712
  8. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  9. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  10. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  11. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  12. 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,818
38 more
  1. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  2. 2016Variational Graph Auto-Encoderscited by 1,373
  3. 2017Random Erasing Data Augmentationcited by 1,235
  4. 2020Graph Contrastive Learning with Augmentationscited by 1,203
  5. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  6. 2018Deep Graph Infomaxcited by 1,101
  7. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
  8. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
  9. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 800
  10. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 784
  11. 2019DropEdge: Towards Deep Graph Convolutional Networks on Node Classificationcited by 679
  12. 2020Contrastive Multi-View Representation Learning on Graphscited by 651
  13. 2019Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological Viewcited by 544
  14. 2020Deep Graph Contrastive Representation Learningcited by 535
  15. 2020Graph Contrastive Learning with Adaptive Augmentationcited by 519
  16. 2021Understanding over-squashing and bottlenecks on graphs via curvaturecited by 471
  17. 2019Diffusion Improves Graph Learningcited by 414
  18. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
  19. 2018Adversarial Attacks on Neural Networks for Graph Datacited by 395
  20. 2020Graph Structure Learning for Robust Graph Neural Networkscited by 368
  21. 2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 328
  22. 2018Adversarial Attack on Graph Structured Datacited by 300
  23. 2021Graph Contrastive Learning Automatedcited by 287
  24. 2019Adversarial Attacks on Graph Neural Networks via Meta Learningcited by 261
  25. 2019Learning Discrete Structures for Graph Neural Networkscited by 228
  26. 2020Graph Random Neural Network for Semi-Supervised Learning on Graphscited by 213
  27. 2020Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddingscited by 212
  28. 2017CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filterscited by 203
  29. 2020Data Augmentation for Graph Neural Networkscited by 200
  30. 2021Adversarial Graph Augmentation to Improve Graph Contrastive Learningcited by 200
  31. 2019Topology Attack and Defense for Graph Neural Networks: An Optimization Perspectivecited by 196
  32. 2020GNNGuard: Defending Graph Neural Networks against Adversarial Attackscited by 195
  33. 2022Discovering Invariant Rationales for Graph Neural Networkscited by 186
  34. 2021Large-Scale Representation Learning on Graphs via Bootstrappingcited by 184
  35. 2021Self-Supervised Learning of Graph Neural Networks: A Unified Reviewcited by 184
  36. 2022Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanismcited by 171
  37. 2021Discrete Graph Structure Learning for Forecasting Multiple Time Seriescited by 169
  38. 2021Graph Condensation for Graph Neural Networkscited by 150

50 most cited of 99 All 99 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 22; 2023: 13; 2024: 15; 2025: 3; 2026: 8.

61 papers.

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  1. 2022Condensing Graphs via One-Step Gradient Matchingcited by 100
  2. 2022Graph Rationalization with Environment-based Augmentationscited by 70
  3. 2022Empowering Graph Representation Learning with Test-Time Graph Transformationcited by 66
  4. 2022A Survey of Graph Neural Networks for Social Recommender Systemscited by 65
  5. 2024A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensationcited by 57
  6. 2022Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented Viewcited by 54
  7. 2022Linkless Link Prediction via Relational Distillationcited by 39
  8. 2022Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shiftcited by 38
  9. 2024A Comprehensive Survey on Data Augmentationcited by 38
  10. 2022Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalizationcited by 35
  11. 2022MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initializationcited by 31
  12. 2023GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentationcited by 29
38 more
  1. 2022Automated Data Augmentations for Graph Classificationcited by 19
  2. 2022Feature Overcorrelation in Deep Graph Neural Networks: A New Perspectivecited by 19
  3. 2023Data-Centric Learning from Unlabeled Graphs with Diffusion Modelcited by 19
  4. 2023Toward Degree Bias in Embedding-Based Knowledge Graph Completioncited by 17
  5. 2024A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptationcited by 16
  6. 2022GraphTTA: Test Time Adaptation on Graph Neural Networkscited by 14
  7. 2023Semi-Supervised Graph Imbalanced Regressioncited by 14
  8. 2022GraphMAD: Graph Mixup for Data Augmentation using Data-Driven Convex Clusteringcited by 13
  9. 2022FakeEdge: Alleviate Dataset Shift in Link Predictioncited by 12
  10. 2022Link Prediction with Non-Contrastive Learningcited by 11
  11. 2022Data Augmentation on Graphs: A Technical Surveycited by 10
  12. 2024NAT-NL2GQL: A Novel Multi-Agent Framework for Translating Natural Language to Graph Query Languagecited by 9
  13. 2023Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matchingcited by 8
  14. 2022Time-aware Random Walk Diffusion to Improve Dynamic Graph Learningcited by 7
  15. 2023CARL-G: Clustering-Accelerated Representation Learning on Graphscited by 5
  16. 2024How Does Message Passing Improve Collaborative Filtering?cited by 5
  17. 2023A Unified Framework for Generative Data Augmentation: A Comprehensive Surveycited by 4
  18. 2024Node Duplication Improves Cold-start Link Predictioncited by 4
  19. 2025Diffusion on Graph: Augmentation of Graph Structure for Node Classificationcited by 4
  20. 2022Causally-guided Regularization of Graph Attention Improves Generalizabilitycited by 3
  21. 2023Link Prediction without Graph Neural Networkscited by 3
  22. 2026Data Augmentation: A Fourier Analysis Perspectivecited by 3
  23. 2022SoftEdge: Regularizing Graph Classification with Random Soft Edgescited by 2
  24. 2022On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processingcited by 2
  25. 2023Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative Studycited by 2
  26. 2023Perfect Alignment May be Poisonous to Graph Contrastive Learningcited by 2
  27. 2024Through the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph Classificationcited by 2
  28. 2024Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attackcited by 2
  29. 2025Principled Data Augmentation for Learning to Solve Quadratic Programming Problemscited by 2
  30. 2023Centrality-Based Node Feature Augmentation for Robust Network Alignmentcited by 1
  31. 2024Rethinking Spectral Augmentation for Contrast-based Graph Self-Supervised Learningcited by 1
  32. 2024Inference of Sequential Patterns for Neural Message Passing in Temporal Graphscited by 1
  33. 2024Long-context Protein Language Modeling Using Bidirectional Mamba with Shared Projection Layerscited by 1
  34. 2024Graph Community Augmentation with GMM-based Modeling in Latent Spacecited by 1
  35. 2022Are Your Reviewers Being Treated Equally? Discovering Subgroup Structures to Improve Fairness in Spam Detectioncited by 0
  36. 2022Edge Augmentation on Disconnected Graphs via Eigenvalue Elevationcited by 0
  37. 2023Structural Imbalance Aware Graph Augmentation Learningcited by 0
  38. 2023GDM: Dual Mixup for Graph Classification with Limited Supervisioncited by 0

50 most cited of 61 All 61 (JSON)