DeepSTGCN: Enhancing Network Traffic Forecasting with a Spatiotemporal Graph Convolutional Network

Abstract

Accurate network traffic prediction is crucial for modern network management. To overcome the limitations of traditional models in handling the nonlinear and multiscale dynamics of real traffic, this paper proposes deep spatiotemporal graph convolutional network (DeepSTGCN). Its core design integrates a dual-channel graph convolution framework, which captures linear and nonlinear spatial dependencies using Pearson and Spearman correlation-based graphs, and a temporal module with stacked gated recurrent units and attention. We further introduce a graph contrastive learning mechanism to enhance model robustness and generalizability by enforcing node embedding consistency under perturbations. Evaluated on multiple real-world datasets, DeepSTGCN outperforms state-of-the-art baselines in accuracy and reliability. Ablation studies confirm the contribution of each component. The model offers a robust solution for traffic forecasting, with strong potential to improve proactive resource allocation and quality of service.