Streaming Graph Learning in IoT With Storage Optimization and Communication Reduction

Graph neural networks (GNNs) have shown great success in IoT applications, but many IoT scenarios further involve evolving graph data over time. Streaming graph learning (SGL) tackles the issue by updating GNN models continuously, incorporating significant historical node data for tasks like node classification. However, existing research on SGL often neglects memory limitations during historical node selection, and efficient distributed training is challenging due to the coupling of node selection, placement, and parallelization. This article proposes an efficient distributed SGL system that optimizes node selection and storage in multi-GPU environments, while reducing communication overhead to accelerate training. Extensive evaluations demonstrate the effectiveness of the approach.