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Degree-Quant: Quantization-Aware Training for Graph Neural Networks

arXiv 2008.05000 ↗First posted 11 Aug 2020cs.LG stat.MLcited by 93

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Abstract (arXiv)

Graph neural networks (GNNs) have demonstrated strong performance on a wide variety of tasks due to their ability to model non-uniform structured data. Despite their promise, there exists little research exploring methods to make them more efficient at inference time. In this work, we explore the viability of training quantized GNNs, enabling the usage of low precision integer arithmetic during inference. We identify the sources of error that uniquely arise when attempting to quantize GNNs, and propose an architecturally-agnostic method, Degree-Quant, to improve performance over existing quantization-aware training baselines commonly used on other architectures, such as CNNs. We validate our method on six datasets and show, unlike previous attempts, that models generalize to unseen graphs. Models trained with Degree-Quant for INT8 quantization perform as well as FP32 models in most cases; for INT4 models, we obtain up to 26% gains over the baselines. Our work enables up to 4.7x speedups on CPU when using INT8 arithmetic.

Key facts. Cited by 93 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Aug 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 15; 2022: 12; 2023: 24; 2024: 19; 2025: 13; 2026: 10.

93 papers.

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  1. 2021A Survey of Quantization Methods for Efficient Neural Network Inferencecited by 798
  2. 2023A Survey on Model Compression for Large Language Modelscited by 263
  3. 2021Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillationcited by 151
  4. 2021A Unified Lottery Ticket Hypothesis for Graph Neural Networkscited by 118
  5. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  6. 2021A Survey on Green Deep Learningcited by 52
  7. 2021VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantizationcited by 39
  8. 2022Linkless Link Prediction via Relational Distillationcited by 39
  9. 2021On Representation Knowledge Distillation for Graph Neural Networkscited by 38
  10. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 38
  11. 2021Federated Graph Learning -- A Position Papercited by 34
  12. 2024A Comprehensive Study on Quantization Techniques for Large Language Modelscited by 34
38 more
  1. 2022Distributed Graph Neural Network Training: A Surveycited by 32
  2. 2023Graph Condensation for Inductive Node Representation Learningcited by 31
  3. 2021Early-Bird GCNs: Graph-Network Co-Optimization Towards More Efficient GCN Training and Inference via Drawing Early-Bird Lottery Ticketscited by 27
  4. 2024ApiQ: Finetuning of 2-Bit Quantized Large Language Modelcited by 27
  5. 2022Survey on Graph Neural Network Acceleration: An Algorithmic Perspectivecited by 26
  6. 2022F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantizationcited by 26
  7. 2024Tender: Accelerating Large Language Models via Tensor Decomposition and Runtime Requantizationcited by 22
  8. 2023Post-training Quantization for Text-to-Image Diffusion Models with Progressive Calibration and Activation Relaxingcited by 21
  9. 2024Faster and Lighter LLMs: A Survey on Current Challenges and Way Forwardcited by 20
  10. 2022Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networkscited by 18
  11. 2023A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardwarecited by 18
  12. 2024EasyQuant: An Efficient Data-free Quantization Algorithm for LLMscited by 17
  13. 2023Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networkscited by 16
  14. 2023One-Shot Sensitivity-Aware Mixed Sparsity Pruning for Large Language Modelscited by 16
  15. 2021G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiencycited by 15
  16. 2023Patch-wise Mixed-Precision Quantization of Vision Transformercited by 15
  17. 2023The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Surveycited by 13
  18. 2021GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Designcited by 12
  19. 2022SPIQ: Data-Free Per-Channel Static Input Quantizationcited by 12
  20. 2022SA-MLP: Distilling Graph Knowledge from GNNs into Structure-Aware MLPcited by 12
  21. 2025Low-bit Model Quantization for Deep Neural Networks: A Surveycited by 11
  22. 2022MKQ-BERT: Quantized BERT with 4-bits Weights and Activationscited by 10
  23. 2023Adaptive Message Quantization and Parallelization for Distributed Full-graph GNN Trainingcited by 10
  24. 2021Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplificationcited by 9
  25. 2023Attend Who is Weak: Enhancing Graph Condensation via Cross-Free Adversarial Trainingcited by 9
  26. 2025Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transformcited by 9
  27. 2026What Makes Low-Bit Quantization-Aware Training Work for Reasoning LLMs? A Systematic Studycited by 9
  28. 2023Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagationcited by 8
  29. 2022Efficient Graph Neural Network Inference at Large Scalecited by 7
  30. 2023EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learningcited by 7
  31. 2025Diffusion Model Quantization: A Reviewcited by 6
  32. 2021Haar Wavelet Feature Compression for Quantized Graph Convolutional Networkscited by 5
  33. 2023Adversarial Erasing with Pruned Elements: Towards Better Graph Lottery Ticketcited by 5
  34. 2023Low-bit Quantization for Deep Graph Neural Networks with Smoothness-aware Message Propagationcited by 5
  35. 2022BiFeat: Supercharge GNN Training via Graph Feature Quantizationcited by 4
  36. 2022Triple Sparsification of Graph Convolutional Networks without Sacrificing the Accuracycited by 4
  37. 2023Tango: rethinking quantization for graph neural network training on GPUscited by 4
  38. 2023MixQuant: Mixed Precision Quantization with a Bit-width Optimization Searchcited by 4

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