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Sampling methods for efficient training of graph convolutional networks: A survey

arXiv 2103.05872 ↗First posted 10 Mar 2021cs.LGcited by 40

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Abstract (arXiv)

Graph Convolutional Networks (GCNs) have received significant attention from various research fields due to the excellent performance in learning graph representations. Although GCN performs well compared with other methods, it still faces challenges. Training a GCN model for large-scale graphs in a conventional way requires high computation and storage costs. Therefore, motivated by an urgent need in terms of efficiency and scalability in training GCN, sampling methods have been proposed and achieved a significant effect. In this paper, we categorize sampling methods based on the sampling mechanisms and provide a comprehensive survey of sampling methods for efficient training of GCN. To highlight the characteristics and differences of sampling methods, we present a detailed comparison within each category and further give an overall comparative analysis for the sampling methods in all categories. Finally, we discuss some challenges and future research directions of the sampling methods.

Key facts. Cited by 40 papers in the map (data through 14 Oct 2026). Builds on 64 of its 99 references that are in the map. First posted on arXiv 10 Mar 2021.

Builds on

64 of 99 references.

References · 64

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38 more
  1. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,209
  2. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
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50 most cited of 64 All 64 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 4; 2022: 9; 2023: 10; 2024: 13; 2025: 3; 2026: 1.

40 papers.

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  1. 2022Simple and Efficient Heterogeneous Graph Neural Networkcited by 71
  2. 2023OpenGSL: A Comprehensive Benchmark for Graph Structure Learningcited by 30
  3. 2022Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysiscited by 29
  4. 2022Survey on Graph Neural Network Acceleration: An Algorithmic Perspectivecited by 26
  5. 2023Data-centric Graph Learning: A Surveycited by 22
  6. 2022Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networkscited by 18
  7. 2023A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardwarecited by 18
  8. 2022A Comprehensive Survey on Distributed Training of Graph Neural Networkscited by 15
  9. 2022Influence-Based Mini-Batching for Graph Neural Networkscited by 15
  10. 2024Even Sparser Graph Transformerscited by 13
  11. 2021Scalable Graph Neural Network Training: The Case for Samplingcited by 11
  12. 2024Repeat-Aware Neighbor Sampling for Dynamic Graph Learningcited by 11
28 more
  1. 2021Multilayer Graph Contrastive Clustering Networkcited by 8
  2. 2021GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardwarecited by 6
  3. 2021GNNear: Accelerating Full-Batch Training of Graph Neural Networks with Near-Memory Processingcited by 6
  4. 2024Precedence-Constrained Winter Value for Effective Graph Data Valuationcited by 6
  5. 2024Task-Oriented GNNs Training on Large Knowledge Graphs for Accurate and Efficient Modelingcited by 5
  6. 2024GraphMuse: A Library for Symbolic Music Graph Processingcited by 5
  7. 2023A Comprehensive Survey on Graph Summarization with Graph Neural Networkscited by 4
  8. 2024Survey of Graph Neural Network for Internet of Things and NextG Networkscited by 4
  9. 2024Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Studycited by 4
  10. 2022Spatio-Temporal Graph Representation Learning for Fraudster Group Detectioncited by 3
  11. 2023Single Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learningcited by 3
  12. 2024Foundations and Frontiers of Graph Learning Theorycited by 3
  13. 2022Analyzing the Effect of Sampling in GNNs on Individual Fairnesscited by 2
  14. 2023InkStream: Real-time GNN Inference on Streaming Graphs via Incremental Updatecited by 2
  15. 2023A Local Graph Limits Perspective on Sampling-Based GNNscited by 2
  16. 2024Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attackcited by 2
  17. 2025Efficient GNN Training Through Structure-Aware Randomized Mini-Batchingcited by 2
  18. 2022Rethinking Efficiency and Redundancy in Training Large-scale Graphscited by 1
  19. 2024Characterizing and Understanding HGNN Training on GPUscited by 1
  20. 2024Graph Neural Networks on Graph Databasescited by 1
  21. 2025Plexus: Taming Billion-edge Graphs with 3D Parallel Full-graph GNN Trainingcited by 1
  22. 2023Graph Entropy Minimization for Semi-supervised Node Classificationcited by 0
  23. 2023PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networkscited by 0
  24. 2023GreenFlow: A Computation Allocation Framework for Building Environmentally Sound Recommendation Systemcited by 0
  25. 2024Topological safeguard for evasion attack interpreting the neural networks' behaviorcited by 0
  26. 2024Vision Paper: Designing Graph Neural Networks in Compliance with the European Artificial Intelligence Actcited by 0
  27. 2025Adaptive Graph Coarsening for Efficient GNN Trainingcited by 0
  28. 2026EmbedPart: Embedding-Driven Graph Partitioning for Scalable Graph Neural Network Trainingcited by 0