A Graph Neural Network-Based Digital Twin for Network Slicing Management

A scalable DT of network slicing is developed, aiming to capture the intertwined relationships among slices and monitor the end-to-end (E2E) metrics of slices under diverse network environments, and exploits the novel graph neural network model that can learn insights directly from slicing-enabled networks represented by non-Euclidean graph structures.