# SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization

arXiv: https://arxiv.org/abs/2007.05100 · HTML: https://mlsanity.com/paper/2007.05100/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2020.

## Built on by (most cited first, 27 of 27)

- [A Survey on Green Deep Learning](https://mlsanity.com/paper/2111.05193/) (2021)
- [VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization](https://mlsanity.com/paper/2110.14363/) (2021)
- [Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI](https://mlsanity.com/paper/2111.06061/) (2021)
- [Distributed Graph Neural Network Training: A Survey](https://mlsanity.com/paper/2211.00216/) (2022)
- [QGTC: Accelerating Quantized Graph Neural Networks via GPU Tensor Core](https://mlsanity.com/paper/2111.09547/) (2021)
- [A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware](https://mlsanity.com/paper/2306.14052/) (2023)
- [The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey](https://mlsanity.com/paper/2305.13854/) (2023)
- [Adaptive Message Quantization and Parallelization for Distributed Full-graph GNN Training](https://mlsanity.com/paper/2306.01381/) (2023)
- [Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks](https://mlsanity.com/paper/2110.04824/) (2021)
- [Low-bit Quantization for Deep Graph Neural Networks with Smoothness-aware Message Propagation](https://mlsanity.com/paper/2308.14949/) (2023)
- [BiFeat: Supercharge GNN Training via Graph Feature Quantization](https://mlsanity.com/paper/2207.14696/) (2022)
- [Tango: rethinking quantization for graph neural network training on GPUs](https://mlsanity.com/paper/2308.00890/) (2023)
- [MEGA: A Memory-Efficient GNN Accelerator Exploiting Degree-Aware Mixed-Precision Quantization](https://mlsanity.com/paper/2311.09775/) (2023)
- [LW-GCN: A Lightweight FPGA-based Graph Convolutional Network Accelerator](https://mlsanity.com/paper/2111.03184/) (2021)
- [rm A2Q: Aggregation-Aware Quantization for Graph Neural Networks](https://mlsanity.com/paper/2302.00193/) (2023)
- [Boosting Distributed Full-graph GNN Training with Asynchronous One-bit Communication](https://mlsanity.com/paper/2303.01277/) (2023)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [Efficient Mixed Precision Quantization in Graph Neural Networks](https://mlsanity.com/paper/2505.09361/) (2025)
- [Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services](https://mlsanity.com/paper/2307.01684/) (2023)
- [FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs](https://mlsanity.com/paper/2412.20796/) (2024)
- [An LLM-Guided Query-Aware Inference System for GNN Models on Large Knowledge Graphs](https://mlsanity.com/paper/2603.04545/) (2026)
- [Better Schedules for Low Precision Training of Deep Neural Networks](https://mlsanity.com/paper/2403.02243/) (2024)
- [Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferent](https://mlsanity.com/paper/2311.01205/) (2023)
- [Acceleration Algorithms in GNNs: A Survey](https://mlsanity.com/paper/2405.04114/) (2024)
- [Distributed Training of Large Graph Neural Networks with Variable Communication Rates](https://mlsanity.com/paper/2406.17611/) (2024)
- [LoRAP: Low-Rank Aggregation Prompting for Quantized Graph Neural Networks Training](https://mlsanity.com/paper/2601.15079/) (2026)
- [TopGQ: Fast GNN Post-Training Quantization Leveraging Topology Information](https://mlsanity.com/paper/2608.30394/) (2026)

## Abstract (arXiv)

With the increasing popularity of graph-based learning, Graph Neural Networks (GNNs) win lots of attention from the research and industry field because of their high accuracy. However, existing GNNs suffer from high memory footprints (e.g., node embedding features). This high memory footprint hurdles the potential applications towards memory-constrained devices, such as the widely-deployed IoT devices. To this end, we propose a specialized GNN quantization scheme, SGQuant, to systematically reduce the GNN memory consumption. Specifically, we first propose a GNN-tailored quantization algorithm design and a GNN quantization fine-tuning scheme to reduce memory consumption while maintaining accuracy. Then, we investigate the multi-granularity quantization strategy that operates at different levels (components, graph topology, and layers) of GNN computation. Moreover, we offer an automatic bit-selecting (ABS) to pinpoint the most appropriate quantization bits for the above multi-granularity quantizations. Intensive experiments show that SGQuant can effectively reduce the memory footprint from 4.25x to 31.9x compared with the original full-precision GNNs while limiting the accuracy drop to 0.4% on average.