# Binary Graph Neural Networks

arXiv: https://arxiv.org/abs/2012.15823 · HTML: https://mlsanity.com/paper/2012.15823/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2020.

## Built on by (most cited first, 30 of 34)

- [Trustworthy Graph Neural Networks: Aspects, Methods and Trends](https://mlsanity.com/paper/2205.07424/) (2022)
- [A Survey on Green Deep Learning](https://mlsanity.com/paper/2111.05193/) (2021)
- [A comprehensive review of Binary Neural Network](https://mlsanity.com/paper/2110.06804/) (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)
- [BiViT: Extremely Compressed Binary Vision Transformer](https://mlsanity.com/paper/2211.07091/) (2022)
- [Survey on Graph Neural Network Acceleration: An Algorithmic Perspective](https://mlsanity.com/paper/2202.04822/) (2022)
- [Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networks](https://mlsanity.com/paper/2109.12872/) (2021)
- [QGTC: Accelerating Quantized Graph Neural Networks via GPU Tensor Core](https://mlsanity.com/paper/2111.09547/) (2021)
- [Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks](https://mlsanity.com/paper/2207.08629/) (2022)
- [A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware](https://mlsanity.com/paper/2306.14052/) (2023)
- [Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks](https://mlsanity.com/paper/2302.02922/) (2023)
- [SA-MLP: Distilling Graph Knowledge from GNNs into Structure-Aware MLP](https://mlsanity.com/paper/2210.09609/) (2022)
- [Data-Free Adversarial Knowledge Distillation for Graph Neural Networks](https://mlsanity.com/paper/2205.03811/) (2022)
- [Graph-based Knowledge Distillation: A survey and experimental evaluation](https://mlsanity.com/paper/2302.14643/) (2023)
- [A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks](https://mlsanity.com/paper/2305.19306/) (2023)
- [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)
- [Triple Sparsification of Graph Convolutional Networks without Sacrificing the Accuracy](https://mlsanity.com/paper/2208.03559/) (2022)
- [BitGNN: Unleashing the Performance Potential of Binary Graph Neural Networks on GPUs](https://mlsanity.com/paper/2305.02522/) (2023)
- [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)
- [rm A2Q: Aggregation-Aware Quantization for Graph Neural Networks](https://mlsanity.com/paper/2302.00193/) (2023)
- [Projected Stochastic Gradient Descent with Quantum Annealed Binary Gradients](https://mlsanity.com/paper/2310.15128/) (2023)
- [Exploring Sparsity in Graph Transformers](https://mlsanity.com/paper/2312.05479/) (2023)
- [Efficient Mixed Precision Quantization in Graph Neural Networks](https://mlsanity.com/paper/2505.09361/) (2025)
- [Road Extraction from Overhead Images with Graph Neural Networks](https://mlsanity.com/paper/2112.05215/) (2021)
- [Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPU](https://mlsanity.com/paper/2210.03900/) (2022)
- [Unifews: You Need Fewer Operations for Efficient Graph Neural Networks](https://mlsanity.com/paper/2403.13268/) (2024)
- [DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks](https://mlsanity.com/paper/2309.03523/) (2023)

## Abstract (arXiv)

Graph Neural Networks (GNNs) have emerged as a powerful and flexible framework for representation learning on irregular data. As they generalize the operations of classical CNNs on grids to arbitrary topologies, GNNs also bring much of the implementation challenges of their Euclidean counterparts. Model size, memory footprint, and energy consumption are common concerns for many real-world applications. Network binarization allocates a single bit to parameters and activations, thus dramatically reducing the memory requirements (up to 32x compared to single-precision floating-point numbers) and maximizing the benefits of fast SIMD instructions on modern hardware for measurable speedups. However, in spite of the large body of work on binarization for classical CNNs, this area remains largely unexplored in geometric deep learning. In this paper, we present and evaluate different strategies for the binarization of graph neural networks. We show that through careful design of the models, and control of the training process, binary graph neural networks can be trained at only a moderate cost in accuracy on challenging benchmarks. In particular, we present the first dynamic graph neural network in Hamming space, able to leverage efficient k-NN search on binary vectors to speed-up the construction of the dynamic graph. We further verify that the binary models offer significant savings on embedded devices. Our code is publicly available on Github.