mlsanityData to 7 Oct 2026

Graph Augmentation Learning

arXiv 2203.09020 ↗First posted 17 Mar 2022cs.LG cs.AIcited by 9

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Abstract (arXiv)

Graph Augmentation Learning (GAL) provides outstanding solutions for graph learning in handling incomplete data, noise data, etc. Numerous GAL methods have been proposed for graph-based applications such as social network analysis and traffic flow forecasting. However, the underlying reasons for the effectiveness of these GAL methods are still unclear. As a consequence, how to choose optimal graph augmentation strategy for a certain application scenario is still in black box. There is a lack of systematic, comprehensive, and experimentally validated guideline of GAL for scholars. Therefore, in this survey, we in-depth review GAL techniques from macro (graph), meso (subgraph), and micro (node/edge) levels. We further detailedly illustrate how GAL enhance the data quality and the model performance. The aggregation mechanism of augmentation strategies and graph learning models are also discussed by different application scenarios, i.e., data-specific, model-specific, and hybrid scenarios. To better show the outperformance of GAL, we experimentally validate the effectiveness and adaptability of different GAL strategies in different downstream tasks. Finally, we share our insights on several open issues of GAL, including heterogeneity, spatio-temporal dynamics, scalability, and generalization.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 52 of its 69 references that are in the map. First posted on arXiv 17 Mar 2022.

Builds on

52 of 69 references.

References · 52

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2018How Powerful are Graph Neural Networks?cited by 4,355
  5. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  6. 2020Graph Contrastive Learning with Augmentationscited by 1,203
  7. 2018Deep Graph Infomaxcited by 1,101
  8. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
  9. 2019Strategies for Pre-training Graph Neural Networkscited by 964
  10. 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 878
  11. 2020Simple and Deep Graph Convolutional Networkscited by 857
  12. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
38 more
  1. 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 718
  2. 2019DropEdge: Towards Deep Graph Convolutional Networks on Node Classificationcited by 679
  3. 2019Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological Viewcited by 544
  4. 2020Graph Contrastive Learning with Adaptive Augmentationcited by 519
  5. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
  6. 2019Provably Powerful Graph Networkscited by 385
  7. 2020Graph Structure Learning for Robust Graph Neural Networkscited by 368
  8. 2020GPT-GNN: Generative Pre-Training of Graph Neural Networkscited by 302
  9. 2020Towards Deeper Graph Neural Networkscited by 296
  10. 2021Graph Self-Supervised Learning: A Surveycited by 289
  11. 2021Graph Contrastive Learning Automatedcited by 287
  12. 2020Time Series Data Augmentation for Deep Learning: A Surveycited by 225
  13. 2020Data Augmentation for Graph Neural Networkscited by 200
  14. 2021Adversarial Graph Augmentation to Improve Graph Contrastive Learningcited by 200
  15. 2021Identity-aware Graph Neural Networkscited by 197
  16. 2021Self-Supervised Learning of Graph Neural Networks: A Unified Reviewcited by 184
  17. 2021Graph Learning: A Surveycited by 140
  18. 2021Nested Graph Neural Networkscited by 136
  19. 2020Learning to Drop: Robust Graph Neural Network via Topological Denoisingcited by 127
  20. 2020When Does Self-Supervision Help Graph Convolutional Networks?cited by 121
  21. 2020AM-GCN: Adaptive Multi-channel Graph Convolutional Networkscited by 121
  22. 2021DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networkscited by 117
  23. 2020GraphNorm: A Principled Approach to Accelerating Graph Neural Network Trainingcited by 116
  24. 2020Towards Deeper Graph Neural Networks with Differentiable Group Normalizationcited by 113
  25. 2019Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labelscited by 111
  26. 2019Graph Adversarial Training: Dynamically Regularizing Based on Graph Structurecited by 93
  27. 2020Contrastive Self-supervised Learning for Graph Classificationcited by 68
  28. 2020Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networkscited by 57
  29. 2020Learning on Attribute-Missing Graphscited by 42
  30. 2020GraphCrop: Subgraph Cropping for Graph Classificationcited by 31
  31. 2020Shifu2: A Network Representation Learning Based Model for Advisor-advisee Relationship Miningcited by 20
  32. 2021Training Robust Graph Neural Networks with Topology Adaptive Edge Droppingcited by 20
  33. 2020Deep Graph Neural Networks with Shallow Subgraph Samplerscited by 16
  34. 2019Power up! Robust Graph Convolutional Network via Graph Poweringcited by 15
  35. 2020Multivariate Relations Aggregation Learning in Social Networkscited by 10
  36. 2022Web of Scholars: A Scholar Knowledge Graphcited by 6
  37. 2020OFFER: A Motif Dimensional Framework for Network Representation Learningcited by 5
  38. 2022Data-Driven Decision Making in COVID-19 Response: A Surveycited by 5

50 most cited of 52 All 52 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 4; 2024: 2; 2025: 0; 2026: 1.

9 papers.

  1. 2023Improving Generalizability of Graph Anomaly Detection Models via Data Augmentationcited by 11
  2. 2022Data Augmentation on Graphs: A Technical Surveycited by 10
  3. 2023RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classificationcited by 7
  4. 2024ADEdgeDrop: Adversarial Edge Dropping for Robust Graph Neural Networkscited by 7
  5. 2023Quantum Graph Learning: Frontiers and Outlookcited by 6
  6. 2023Towards Long-Tailed Recognition for Graph Classification via Collaborative Expertscited by 5
  7. 2024textttSynC: Synergistic Boosting of Structure and Representation for Deep Graph Clusteringcited by 2
  8. 2022Improving Generalizability of Graph Anomaly Detection Models via Data Augmentationcited by 0
  9. 2026An Information Theoretic Framework for Graph Novelty Generation via Latent Mixture Modelingcited by 0