# Bi-GCN: Binary Graph Convolutional Network

arXiv: https://arxiv.org/abs/2010.07565 · HTML: https://mlsanity.com/paper/2010.07565/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2020.

## Built on by (most cited first, 28 of 28)

- [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)
- [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)
- [Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks](https://mlsanity.com/paper/2207.08629/) (2022)
- [Understanding Heterophily for Graph Neural Networks](https://mlsanity.com/paper/2401.09125/) (2024)
- [Model-Architecture Co-Design for High Performance Temporal GNN Inference on FPGA](https://mlsanity.com/paper/2203.05095/) (2022)
- [Enabling Homogeneous GNNs to Handle Heterogeneous Graphs via Relation Embedding](https://mlsanity.com/paper/2209.11414/) (2022)
- [Heterophily-Aware Graph Attention Network](https://mlsanity.com/paper/2302.03228/) (2023)
- [A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks](https://mlsanity.com/paper/2305.19306/) (2023)
- [Triple Sparsification of Graph Convolutional Networks without Sacrificing the Accuracy](https://mlsanity.com/paper/2208.03559/) (2022)
- [Binary Graph Convolutional Network with Capacity Exploration](https://mlsanity.com/paper/2210.13149/) (2022)
- [BitGNN: Unleashing the Performance Potential of Binary Graph Neural Networks on GPUs](https://mlsanity.com/paper/2305.02522/) (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)
- [Binarized Simplicial Convolutional Neural Networks](https://mlsanity.com/paper/2405.04098/) (2024)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [Road Extraction from Overhead Images with Graph Neural Networks](https://mlsanity.com/paper/2112.05215/) (2021)
- [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)
- [Boosting Binary Neural Networks via Dynamic Thresholds Learning](https://mlsanity.com/paper/2211.02292/) (2022)
- [Acceleration Algorithms in GNNs: A Survey](https://mlsanity.com/paper/2405.04114/) (2024)
- [Neighbor-Sampling Based Momentum Stochastic Methods for Training Graph Neural Networks](https://mlsanity.com/paper/2508.00267/) (2025)
- [A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection](https://mlsanity.com/paper/2510.26307/) (2025)
- [GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA](https://mlsanity.com/paper/2604.21290/) (2026)
- [Extremely Fast and Compact Binary Graph Representations via Randomized Operator Sketching](https://mlsanity.com/paper/2609.32641/) (2026)

## Abstract (arXiv)

Graph Neural Networks (GNNs) have achieved tremendous success in graph representation learning. Unfortunately, current GNNs usually rely on loading the entire attributed graph into network for processing. This implicit assumption may not be satisfied with limited memory resources, especially when the attributed graph is large. In this paper, we pioneer to propose a Binary Graph Convolutional Network (Bi-GCN), which binarizes both the network parameters and input node features. Besides, the original matrix multiplications are revised to binary operations for accelerations. According to the theoretical analysis, our Bi-GCN can reduce the memory consumption by an average of ~30x for both the network parameters and input data, and accelerate the inference speed by an average of ~47x, on the citation networks. Meanwhile, we also design a new gradient approximation based back-propagation method to train our Bi-GCN well. Extensive experiments have demonstrated that our Bi-GCN can give a comparable performance compared to the full-precision baselines. Besides, our binarization approach can be easily applied to other GNNs, which has been verified in the experiments.