High-Degree Node Energy Smearing for Graph Neural Networks quantization

Graph Neural Networks (GNNs) achieve state-of-the-art accuracy on irregularly structured data, yet efficient deployment on resource-constrained platforms remains challenging. This challenge is growing in importance as GNNs are increasingly incorporated into real-world products. In recent years, graphs underlying real-world applications have increased dramatically in size, making it necessary to operate under strict energy, latency, and memory constraints. Quantization has emerged as a promising approach to address these challenges. However, its accuracy degradation in message-passing GNNs is often severe. We show that this degradation is fundamentally topological: activation energy grows with node degree, causing hub nodes to dominate the dynamic range and reducing quantization resolution. Existing methods treat quantization error as a calibration or smoothness problem and fail to address this deterministic range explosion. We propose High-Degree Node Energy Smearing (HD-NES), a topology-aware method that applies lightweight, reversible rotation transformations to diffuse the feature energy of hub nodes. By reducing activation variance while preserving information via reversibility, HD-NES mitigates hub-induced outliers and enables accurate low-bit quantization. HD-NES integrates into existing GNN quantization pipelines without architectural changes and consistently outperforms prior approaches.