mlsanityData to 7 Oct 2026

Graph Condensation for Graph Neural Networks

arXiv 2110.07580 ↗First posted 14 Oct 2021cs.LG cs.AIcited by 150

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Abstract (arXiv)

Given the prevalence of large-scale graphs in real-world applications, the storage and time for training neural models have raised increasing concerns. To alleviate the concerns, we propose and study the problem of graph condensation for graph neural networks (GNNs). Specifically, we aim to condense the large, original graph into a small, synthetic and highly-informative graph, such that GNNs trained on the small graph and large graph have comparable performance. We approach the condensation problem by imitating the GNN training trajectory on the original graph through the optimization of a gradient matching loss and design a strategy to condense node futures and structural information simultaneously. Extensive experiments have demonstrated the effectiveness of the proposed framework in condensing different graph datasets into informative smaller graphs. In particular, we are able to approximate the original test accuracy by 95.3% on Reddit, 99.8% on Flickr and 99.0% on Citeseer, while reducing their graph size by more than 99.9%, and the condensed graphs can be used to train various GNN architectures.Code is released at https://github.com/ChandlerBang/GCond.

Key facts. Cited by 150 papers in the map (data through 7 Oct 2026). Builds on 40 of its 61 references that are in the map. First posted on arXiv 14 Oct 2021.

Builds on

40 of 61 references.

References · 40

  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. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  5. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  6. 2016iCaRL: Incremental Classifier and Representation Learningcited by 2,313
  7. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  8. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  9. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  10. 2019Simplifying Graph Convolutional Networkscited by 1,470
  11. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  12. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
28 more
  1. 2019Deep Leakage from Gradientscited by 1,004
  2. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
  3. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 786
  4. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
  5. 2019Graph Neural Networks for Social Recommendationcited by 711
  6. 2019GraphSAINT: Graph Sampling Based Inductive Learning Methodcited by 618
  7. 2018End-to-End Incremental Learningcited by 596
  8. 2019DeepGCNs: Can GCNs Go as Deep as CNNs?cited by 527
  9. 2020Dataset Condensation with Gradient Matchingcited by 510
  10. 2019Graph U-Netscited by 489
  11. 2020Graph Structure Learning for Robust Graph Neural Networkscited by 368
  12. 2020Towards Deeper Graph Neural Networkscited by 296
  13. 2021Dataset Condensation with Differentiable Siamese Augmentationcited by 290
  14. 2021Graph Contrastive Learning Automatedcited by 287
  15. 2020Dataset Meta-Learning from Kernel Ridge-Regressioncited by 230
  16. 2019Learning Discrete Structures for Graph Neural Networkscited by 228
  17. 2020Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddingscited by 212
  18. 2020Combining Label Propagation and Simple Models Out-performs Graph Neural Networkscited by 199
  19. 2021From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awarenesscited by 154
  20. 2021Interpreting and Unifying Graph Neural Networks with An Optimization Frameworkcited by 131
  21. 2020A Unified View on Graph Neural Networks as Graph Signal Denoisingcited by 110
  22. 2020Flexible Dataset Distillation: Learn Labels Instead of Imagescited by 91
  23. 2021Scaling Up Graph Neural Networks Via Graph Coarseningcited by 80
  24. 2021Elastic Graph Neural Networkscited by 67
  25. 2019GraphZoom: A multi-level spectral approach for accurate and scalable graph embeddingcited by 58
  26. 2018Spectrally approximating large graphs with smaller graphscited by 56
  27. 2021Graph Coarsening with Neural Networkscited by 55
  28. 2021Adaptive Label Smoothing To Regularize Large-Scale Graph Trainingcited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 17; 2023: 32; 2024: 48; 2025: 32; 2026: 20.

150 papers.

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  1. 2021Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillationcited by 151
  2. 2022Data Augmentation for Deep Graph Learning: A Surveycited by 132
  3. 2022Dataset Pruning: Reducing Training Data by Examining Generalization Influencecited by 132
  4. 2023Dataset Distillation: A Comprehensive Reviewcited by 126
  5. 2022Privacy for Free: How does Dataset Condensation Help Privacy?cited by 119
  6. 2023Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matchingcited by 118
  7. 2023A Comprehensive Survey of Dataset Distillationcited by 114
  8. 2022Condensing Graphs via One-Step Gradient Matchingcited by 100
  9. 2023Data Distillation: A Surveycited by 86
  10. 2023Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Datacited by 71
  11. 2023Data Pruning via Moving-one-Sample-outcited by 67
  12. 2022Accelerating Dataset Distillation via Model Augmentationcited by 62
38 more
  1. 2022Graph Data Augmentation for Graph Machine Learning: A Surveycited by 61
  2. 2022Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakagecited by 61
  3. 2022Graph Condensation via Receptive Field Distribution Matchingcited by 60
  4. 2024A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensationcited by 57
  5. 2023CaT: Balanced Continual Graph Learning with Graph Condensationcited by 50
  6. 2023Does Graph Distillation See Like Vision Dataset Counterpart?cited by 45
  7. 2023Knowledge Distillation on Graphs: A Surveycited by 38
  8. 2022SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networkscited by 36
  9. 2023Fast Graph Condensation with Structure-based Neural Tangent Kernelcited by 36
  10. 2023A Survey on Dataset Distillation: Approaches, Applications and Future Directionscited by 32
  11. 2022MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initializationcited by 31
  12. 2023Graph Condensation for Inductive Node Representation Learningcited by 31
  13. 2022DropMessage: Unifying Random Dropping for Graph Neural Networkscited by 29
  14. 2024Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matchingcited by 28
  15. 2024Graph Condensation: A Surveycited by 23
  16. 2024Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partitioncited by 23
  17. 2023Data-centric Graph Learning: A Surveycited by 22
  18. 2022Infinite Recommendation Networks: A Data-Centric Approachcited by 20
  19. 2022Efficient Bi-Level Optimization for Recommendation Denoisingcited by 20
  20. 2024EXGC: Bridging Efficiency and Explainability in Graph Condensationcited by 20
  21. 2022Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networkscited by 18
  22. 2023A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardwarecited by 18
  23. 2024Importance-Aware Adaptive Dataset Distillationcited by 18
  24. 2024Graph Data Condensation via Self-expressive Graph Structure Reconstructioncited by 18
  25. 2024Graph Sparsification via Mixture of Graphscited by 17
  26. 2023Graph Collaborative Signals Denoising and Augmentation for Recommendationcited by 16
  27. 2023Graph Distillation with Eigenbasis Matchingcited by 16
  28. 2023A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarseningcited by 15
  29. 2023Towards Data-centric Graph Machine Learning: Review and Outlookcited by 15
  30. 2024Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexitycited by 14
  31. 2023Mirage: Model-Agnostic Graph Distillation for Graph Classificationcited by 13
  32. 2023Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Trainingcited by 12
  33. 2024GC-Bench: An Open and Unified Benchmark for Graph Condensationcited by 12
  34. 2023Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networkscited by 11
  35. 2024Disentangled Condensation for Large-scale Graphscited by 10
  36. 2024Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awarenesscited by 10
  37. 2022A Unified Framework for Optimization-Based Graph Coarseningcited by 9
  38. 2023Attend Who is Weak: Enhancing Graph Condensation via Cross-Free Adversarial Trainingcited by 9

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