mlsanityData to 14 Oct 2026

GraphSAINT: Graph Sampling Based Inductive Learning Method

arXiv 1907.04931 ↗First posted 10 Jul 2019cs.LG stat.MLcited by 618

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Abstract (arXiv)

Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer sampling techniques to alleviate the "neighbor explosion" problem during minibatch training. We propose GraphSAINT, a graph sampling based inductive learning method that improves training efficiency and accuracy in a fundamentally different way. By changing perspective, GraphSAINT constructs minibatches by sampling the training graph, rather than the nodes or edges across GCN layers. Each iteration, a complete GCN is built from the properly sampled subgraph. Thus, we ensure fixed number of well-connected nodes in all layers. We further propose normalization technique to eliminate bias, and sampling algorithms for variance reduction. Importantly, we can decouple the sampling from the forward and backward propagation, and extend GraphSAINT with many architecture variants (e.g., graph attention, jumping connection). GraphSAINT demonstrates superior performance in both accuracy and training time on five large graphs, and achieves new state-of-the-art F1 scores for PPI (0.995) and Reddit (0.970).

Key facts. Cited by 618 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Jul 2019.

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Citing papers per year: 2021: 104; 2022: 139; 2023: 124; 2024: 111; 2025: 80; 2026: 60.

618 papers.

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  1. 2021How Attentive are Graph Attention Networks?cited by 835
  2. 2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 329
  3. 2021Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodscited by 311
  4. 2023NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classificationcited by 210
  5. 2021Self-Supervised Learning of Graph Neural Networks: A Unified Reviewcited by 184
  6. 2021Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learningcited by 177
  7. 2022Graph Neural Networks for Graphs with Heterophily: A Surveycited by 175
  8. 2021Training Graph Neural Networks with 1000 Layerscited by 169
  9. 2021Equivariant Subgraph Aggregation Networkscited by 154
  10. 2021Graph Condensation for Graph Neural Networkscited by 150
  11. 2022GOOD: A Graph Out-of-Distribution Benchmarkcited by 141
  12. 2021Graph Unlearningcited by 138
38 more
  1. 2021Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Predictioncited by 138
  2. 2022NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphscited by 137
  3. 2022Data Augmentation for Deep Graph Learning: A Surveycited by 133
  4. 2023SGFormer: Simplifying and Empowering Transformers for Large-Graph Representationscited by 133
  5. 2023GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learnercited by 132
  6. 2023Similarity of Neural Network Models: A Survey of Functional and Representational Measurescited by 131
  7. 2021You are AllSet: A Multiset Function Framework for Hypergraph Neural Networkscited by 127
  8. 2022How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervisioncited by 102
  9. 2022Condensing Graphs via One-Step Gradient Matchingcited by 100
  10. 2022Decoupling the Depth and Scope of Graph Neural Networkscited by 94
  11. 2021GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddingscited by 90
  12. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 89
  13. 2023DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusioncited by 87
  14. 2024Position: Graph Foundation Models are Already Herecited by 86
  15. 2022Graph Attention Multi-Layer Perceptroncited by 83
  16. 2021Graph Representation Learning in Biomedicinecited by 80
  17. 2021Scaling Up Graph Neural Networks Via Graph Coarseningcited by 80
  18. 2022BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphscited by 80
  19. 2023SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learningcited by 73
  20. 2021ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learningcited by 72
  21. 2023Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Datacited by 71
  22. 2022A Survey on Graph Representation Learning Methodscited by 66
  23. 2021LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysiscited by 65
  24. 2022Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observationscited by 64
  25. 2021Gophormer: Ego-Graph Transformer for Node Classificationcited by 63
  26. 2022Understanding and Improving Graph Injection Attack by Promoting Unnoticeabilitycited by 62
  27. 2022Graph Condensation via Receptive Field Distribution Matchingcited by 60
  28. 2022Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyondcited by 60
  29. 2021Dissecting the Diffusion Process in Linear Graph Convolutional Networkscited by 58
  30. 2022A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinkingcited by 56
  31. 2021Graph Coarsening with Neural Networkscited by 55
  32. 2021Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced trainingcited by 54
  33. 2022Neighborhood-aware Scalable Temporal Network Representation Learningcited by 54
  34. 2025Position: Graph Learning Will Lose Relevance Due To Poor Benchmarkscited by 53
  35. 2022Scalable Spatiotemporal Graph Neural Networkscited by 52
  36. 2025PyG 2.0: Scalable Learning on Real World Graphscited by 52
  37. 2021BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessingcited by 50
  38. 2022A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphscited by 49

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