Multirepresentation Spatial-Temporal Graph Convolutional Networks for Network Traffic Prediction

With the rapid proliferation of the Internet of Things (IoT), network traffic prediction has become crucial for intelligent network management, enabling more reliable and flexible services for a vast array of IoT devices and applications. The heterogeneous and dynamic nature of IoT networks introduces complex spatial relations and underlying periodic dependencies in spatial-temporal graphs that existing methods struggle to model effectively. In this article, we propose multirepresentation spatial-temporal graph convolutional networks (MRSTGCNs), a novel unified framework specifically designed to address these challenges. MRSTGCN integrates a multirepresentation graph convolutional network (MRGCN) module to model node heterogeneity and complex traffic propagation, and two complementary embedding modules-Historical Embedding and Temporal Embedding-to capture and fuse periodic dependencies across different fine-grained temporal cycles. Extensive experiments are conducted on two network traffic datasets, and the results demonstrate that MRSTGCN achieves state-of-the-art performance with obvious improvements in MAE, RMSE and MAPE on three prediction horizons.