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FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling

arXiv 1801.10247 ↗First posted 30 Jan 2018cs.LGcited by 583

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Abstract (arXiv)

The graph convolutional networks (GCN) recently proposed by Kipf and Welling are an effective graph model for semi-supervised learning. This model, however, was originally designed to be learned with the presence of both training and test data. Moreover, the recursive neighborhood expansion across layers poses time and memory challenges for training with large, dense graphs. To relax the requirement of simultaneous availability of test data, we interpret graph convolutions as integral transforms of embedding functions under probability measures. Such an interpretation allows for the use of Monte Carlo approaches to consistently estimate the integrals, which in turn leads to a batched training scheme as we propose in this work---FastGCN. Enhanced with importance sampling, FastGCN not only is efficient for training but also generalizes well for inference. We show a comprehensive set of experiments to demonstrate its effectiveness compared with GCN and related models. In particular, training is orders of magnitude more efficient while predictions remain comparably accurate.

Key facts. Cited by 583 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Jan 2018.

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Citing papers per year: 2021: 155; 2022: 140; 2023: 117; 2024: 84; 2025: 58; 2026: 29.

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  1. 2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 328
  2. 2021Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodscited by 309
  3. 2024LLaGA: Large Language and Graph Assistantcited by 177
  4. 2021Training Graph Neural Networks with 1000 Layerscited by 169
  5. 2022How Powerful are Spectral Graph Neural Networkscited by 169
  6. 2021Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillationcited by 151
  7. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  8. 2022NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphscited by 137
  9. 2022Data Augmentation for Deep Graph Learning: A Surveycited by 132
  10. 2023GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learnercited by 131
  11. 2021ETA Prediction with Graph Neural Networks in Google Mapscited by 130
  12. 2021A Unified Lottery Ticket Hypothesis for Graph Neural Networkscited by 118
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  1. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  2. 2021Robustness of Graph Neural Networks at Scalecited by 100
  3. 2022Decoupling the Depth and Scope of Graph Neural Networkscited by 94
  4. 2021GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddingscited by 90
  5. 2022Graph Attention Multi-Layer Perceptroncited by 82
  6. 2021Scaling Up Graph Neural Networks Via Graph Coarseningcited by 80
  7. 2021Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classificationcited by 79
  8. 2021Towards Self-Explainable Graph Neural Networkcited by 75
  9. 2021NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphscited by 73
  10. 2021ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learningcited by 72
  11. 2022Hierarchical Graph Transformer with Adaptive Node Samplingcited by 72
  12. 2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
  13. 2021Lorentzian Graph Convolutional Networkscited by 66
  14. 2022A Survey on Graph Representation Learning Methodscited by 66
  15. 2022FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generationcited by 66
  16. 2022Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labelscited by 63
  17. 2022Structural Entropy Guided Graph Hierarchical Poolingcited by 62
  18. 2021Graph Neural Networks Inspired by Classical Iterative Algorithmscited by 60
  19. 2024LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddingscited by 60
  20. 2021DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networkscited by 59
  21. 2021Dissecting the Diffusion Process in Linear Graph Convolutional Networkscited by 58
  22. 2021Simplifying approach to Node Classification in Graph Neural Networkscited by 56
  23. 2022A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinkingcited by 56
  24. 2021Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depthcited by 55
  25. 2021Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced trainingcited by 54
  26. 2022GREAD: Graph Neural Reaction-Diffusion Networkscited by 54
  27. 2024A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challengescited by 54
  28. 2021BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessingcited by 50
  29. 2022A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphscited by 49
  30. 2025PyG 2.0: Scalable Learning on Real World Graphscited by 49
  31. 2021Node Dependent Local Smoothing for Scalable Graph Learningcited by 45
  32. 2021Improving Subgraph Recognition with Variational Graph Information Bottleneckcited by 45
  33. 2024FedGTA: Topology-aware Averaging for Federated Graph Learningcited by 44
  34. 2022BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Samplingcited by 43
  35. 2022Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPscited by 42
  36. 2021Accelerating Large Scale Real-Time GNN Inference using Channel Pruningcited by 41
  37. 2021Improving Graph Neural Networks with Simple Architecture Designcited by 41
  38. 2021A Survey on Graph-Based Deep Learning for Computational Histopathologycited by 41

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