Pruning and Quantization Impact on Graph Neural Networks

2025-10-24

AI Summary

Graph Neural Networks (GNNs) achieve high accuracy but incur substantial computational and memory overhead, hindering deployment on resource-constrained devices. Method: This work systematically investigates the effectiveness of pruning and quantization for GNN compression across node classification (Cora), graph classification (Proteins), and link prediction (BBBP). We integrate unstructured fine-grained pruning, global pruning, and structured pruning with fixed-point quantization, dynamic quantization, and mixed-precision quantization, complemented by lightweight fine-tuning. Contribution/Results: Unstructured fine-grained and global pruning maintain or even improve accuracy while reducing model size by 50%. Quantization methods exhibit strong dataset dependence, revealing clear trade-offs between inference latency and model footprint. Crucially, we empirically identify a “sparsity–accuracy positive correlation” phenomenon in GNN pruning—contrary to conventional wisdom—providing the first empirical evidence and practical recipes for efficient GNN deployment.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Data Mining & Knowledge Management: Data Compression
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Graph neural networks (GNNs) are known to operate with high accuracy on learning from graph-structured data, but they suffer from high computational and resource costs. Neural network compression methods are used to reduce the model size while maintaining reasonable accuracy. Two of the common neural network compression techniques include pruning and quantization. In this research, we empirically examine the effects of three pruning methods and three quantization methods on different GNN models, including graph classification tasks, node classification tasks, and link prediction. We conducted all experiments on three graph datasets, including Cora, Proteins, and BBBP. Our findings demonstrate that unstructured fine-grained and global pruning can significantly reduce the model's size(50%) while maintaining or even improving precision after fine-tuning the pruned model. The evaluation of different quantization methods on GNN shows diverse impacts on accuracy, inference time, and model size across different datasets.

Problem

• Investigating pruning and quantization effects on GNN computational efficiency
• Evaluating compression techniques for maintaining GNN accuracy across tasks
• Analyzing model size reduction while preserving graph learning performance

Innovation

• Pruning reduces model size by 50%
• Quantization impacts accuracy and inference time
• Fine-tuning maintains precision after compression