Explainable AI Framework for Deep Learning-based Network Digital Twins

Network Digital Twins (NDTs) aim to provide high-fidelity virtual representations of networks, replicating their architecture, components, and real-time dynamics. They enable organizations to analyze, optimize, and predict network performance and behavior under diverse scenarios. As telecommunication networks become increasingly complex – driven by advancements such as edge computing, 5G/6G, and software-defined networking – the need for advanced data analytics techniques like Deep Learning (DL) becomes more pressing. Despite their accuracy and efficiency, DL-based NDTs face limited adoption due to their lack of explainability. To address this challenge, our study builds on previous work that proposed a Graph Neural Network (GNN)-based NDT model by integrating an eXplainable AI (XAI) framework to enhance its interpretability. Our findings reveal the key input-output features that most influence the GNN-based NDT model’s predictions, providing deeper insights into its decision-making process. Additionally, we present practical recommendations for improving network operations, with a particular emphasis on sustainability.