mlsanityData to 7 Oct 2026

Survey on Graph Neural Network Acceleration: An Algorithmic Perspective

arXiv 2202.04822 ↗First posted 10 Feb 2022cs.LG cs.AIcited by 26

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Abstract (arXiv)

Graph neural networks (GNNs) have been a hot spot of recent research and are widely utilized in diverse applications. However, with the use of huger data and deeper models, an urgent demand is unsurprisingly made to accelerate GNNs for more efficient execution. In this paper, we provide a comprehensive survey on acceleration methods for GNNs from an algorithmic perspective. We first present a new taxonomy to classify existing acceleration methods into five categories. Based on the classification, we systematically discuss these methods and highlight their correlations. Next, we provide comparisons from aspects of the efficiency and characteristics of these methods. Finally, we suggest some promising prospects for future research.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 43 of its 68 references that are in the map. First posted on arXiv 10 Feb 2022.

Builds on

43 of 68 references.

References · 43

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  3. 2017Graph Attention Networkscited by 8,854
  4. 2017Inductive Representation Learning on Large Graphscited by 6,284
  5. 2018How Powerful are Graph Neural Networks?cited by 4,355
  6. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  7. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  8. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  9. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  10. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,134
  11. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 735
  12. 2019DropEdge: Towards Deep Graph Convolutional Networks on Node Classificationcited by 679
31 more
  1. 2019Neural Graph Collaborative Filteringcited by 660
  2. 2019GraphSAINT: Graph Sampling Based Inductive Learning Methodcited by 618
  3. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 583
  4. 2020Heterogeneous Graph Transformercited by 581
  5. 2019Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networkscited by 540
  6. 2018Deep Learning on Graphs: A Surveycited by 393
  7. 2017A Survey of Model Compression and Acceleration for Deep Neural Networkscited by 298
  8. 2020Data Augmentation for Graph Neural Networkscited by 200
  9. 2018Adaptive Sampling Towards Fast Graph Representation Learningcited by 180
  10. 2020Scaling Graph Neural Networks with Approximate PageRankcited by 174
  11. 2020DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphscited by 139
  12. 2020Distilling Knowledge from Graph Convolutional Networkscited by 128
  13. 2021A Unified Lottery Ticket Hypothesis for Graph Neural Networkscited by 118
  14. 2020Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspectivecited by 93
  15. 2020Degree-Quant: Quantization-Aware Training for Graph Neural Networkscited by 93
  16. 2021UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendationcited by 87
  17. 2020Computing Graph Neural Networks: A Survey from Algorithms to Acceleratorscited by 81
  18. 2020HyGCN: A GCN Accelerator with Hybrid Architecturecited by 73
  19. 2021Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Frameworkcited by 69
  20. 2021DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networkscited by 59
  21. 2021Graph Learning based Recommender Systems: A Reviewcited by 59
  22. 2021BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessingcited by 50
  23. 2021Sampling methods for efficient training of graph convolutional networks: A surveycited by 40
  24. 2021Automated Machine Learning on Graphs: A Surveycited by 38
  25. 2018Accurate, Efficient and Scalable Graph Embeddingcited by 35
  26. 2020Binary Graph Neural Networkscited by 32
  27. 2020Bi-GCN: Binary Graph Convolutional Networkcited by 27
  28. 2013Recent Advances in Graph Partitioningcited by 24
  29. 2020Characterizing and Understanding GCNs on GPUcited by 22
  30. 2020Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsificationcited by 16
  31. 2021GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardwarecited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 6; 2024: 10; 2025: 3; 2026: 2.

26 papers.

Sort:

  1. 2022Simple and Efficient Heterogeneous Graph Neural Networkcited by 71
  2. 2024A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensationcited by 57
  3. 2022Distributed Graph Neural Network Training: A Surveycited by 32
  4. 2023Data-centric Graph Learning: A Surveycited by 22
  5. 2022Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networkscited by 18
  6. 2023Graph Anomaly Detection in Time Series: A Surveycited by 18
  7. 2023Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detectioncited by 17
  8. 2024Prospects for inconsistency detection using large language models and sheavescited by 10
  9. 2023Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approachcited by 6
  10. 2024Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networkscited by 6
  11. 2024A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectivenesscited by 6
  12. 2025Nonparametric Teaching for Graph Property Learnerscited by 5
14 more
  1. 2024Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Studycited by 4
  2. 2023Exploring Sparsity in Graph Transformerscited by 3
  3. 2024Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attackcited by 2
  4. 2024Unifews: You Need Fewer Operations for Efficient Graph Neural Networkscited by 2
  5. 2022Rethinking Efficiency and Redundancy in Training Large-scale Graphscited by 1
  6. 2024On the Robustness of Graph Reduction Against GNN Backdoorcited by 1
  7. 2022ProGNNosis: A Data-driven Model to Predict GNN Computation Time Using Graph Metricscited by 0
  8. 2023Fast and Effective GNN Training through Sequences of Random Path Graphscited by 0
  9. 2024Teaching MLP More Graph Information: A Three-stage Multitask Knowledge Distillation Frameworkcited by 0
  10. 2024Acceleration Algorithms in GNNs: A Surveycited by 0
  11. 2025Inference-friendly Graph Compression for Graph Neural Networkscited by 0
  12. 2025Demystifying Distributed Training of Graph Neural Networks for Link Predictioncited by 0
  13. 2026EmbedPart: Embedding-Driven Graph Partitioning for Scalable Graph Neural Network Trainingcited by 0
  14. 2026Communication-free Sampling and 4D Hybrid Parallelism for Scalable Mini-batch GNN Trainingcited by 0