Rethinking traffic prediction in Mobile Network Digital Twins: A flexible inductive graph-based learning model for data-scarce scenarios

Network Digital Twins (NDTs) are increasingly relying on data-driven approaches for modeling complex network dynamics. Traffic forecasting is crucial for NDTs to provide timely insights for automated network reconfiguration. Existing spatiotemporal forecasting methods, while effective, often rely on pre-constructed graphs, limiting their flexibility in dynamic network environments. This paper introduces Flex+, an inductive graph-based learning model designed for traffic prediction in data-scarce scenarios. Flex+ focuses on individual eNodeB traffic prediction by extracting local spatial correlations from k-hop subgraphs, combined with temporal information. Its inductive design allows it to operate on unseen nodes during training, enabling adaptability to evolving network topologies. Empirical studies on a large-scale cellular traffic dataset demonstrate that Flex+ achieves a 5.9% improvement in accuracy in inductive settings and a 22% reduction in error in data-scarce scenarios when trained with only 3 days of traffic data. Notably, a Knowledge Distillation (KD) framework is introduced to reduce model size and accelerate inference time up to 10 times while maintaining prediction accuracy.