mlsanityData to 14 Oct 2026

A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware

arXiv 2306.14052 ↗First posted 24 Jun 2023cs.LG cs.AR cs.DCcited by 18

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Abstract (arXiv)

Graph neural networks (GNNs) are emerging for machine learning research on graph-structured data. GNNs achieve state-of-the-art performance on many tasks, but they face scalability challenges when it comes to real-world applications that have numerous data and strict latency requirements. Many studies have been conducted on how to accelerate GNNs in an effort to address these challenges. These acceleration techniques touch on various aspects of the GNN pipeline, from smart training and inference algorithms to efficient systems and customized hardware. As the amount of research on GNN acceleration has grown rapidly, there lacks a systematic treatment to provide a unified view and address the complexity of relevant works. In this survey, we provide a taxonomy of GNN acceleration, review the existing approaches, and suggest future research directions. Our taxonomic treatment of GNN acceleration connects the existing works and sets the stage for further development in this area.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 117 of its 188 references that are in the map. First posted on arXiv 24 Jun 2023.

Builds on

117 of 188 references.

References · 117

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38 more
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50 most cited of 117 All 117 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 9; 2025: 5; 2026: 2.

18 papers.

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  1. 2024A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensationcited by 57
  2. 2024Graph Condensation: A Surveycited by 23
  3. 2023Graph Meets LLMs: Towards Large Graph Modelscited by 20
  4. 2024GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insightscited by 7
  5. 2024A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectivenesscited by 6
  6. 2025Nonparametric Teaching for Graph Property Learnerscited by 5
  7. 2025Scaling Laws of Graph Neural Networks for Atomistic Materials Modelingcited by 4
  8. 2024Unifews: You Need Fewer Operations for Efficient Graph Neural Networkscited by 2
  9. 2025Bit-Flip Fault Attack: Crushing Graph Neural Networks via Gradual Bit Searchcited by 1
  10. 2023An FPGA-Based Accelerator for Graph Embedding using Sequential Training Algorithmcited by 0
  11. 2024Acceleration Algorithms in GNNs: A Surveycited by 0
  12. 2024The Transferability of Downsamped Sparse Graph Convolutional Networkscited by 0
6 more
  1. 2024On the Adversarial Robustness of Graph Neural Networks with Graph Reductioncited by 0
  2. 2024Extending Graph Condensation to Multi-Label Datasets: A Benchmark Studycited by 0
  3. 2025Towards Multimodal Graph Large Language Modelcited by 0
  4. 2025HePGA: A Heterogeneous Processing-in-Memory based GNN Training Acceleratorcited by 0
  5. 2026Not All Neighbors Matter: Understanding the Impact of Graph Sparsification on GNN Pipelinescited by 0
  6. 2026GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGAcited by 0