Graph Neural Network for Digital Twin Network: A Conceptual Framework

Graph Neural Networks (GNNs) have emerged as a powerful framework for analyzing and extracting information from complex network data. In the realm of Digital Twin Networks (DTN), where physical entities are mirrored in a virtual environment, GNNs offer a transformative approach by leveraging the inherent structure and relationships within digital twins. GNNs enable enhanced data representation and predictive modeling. DTNs encompass several core elements that naturally conform to a graph-like structure, including aspects like network topology and routing patterns. In this paper, we review the concept of graph neural network models, network of digital twin applications, and their comparison with other different fields.

I. Introduction

The rise of immersive services, such as virtual reality, augmented reality, holographic content, and metaverse development, is leading to increased complexity in communication networks [1]. To efficiently optimize and manage these intricate networks, a technology known as the digital twin network (DTN) or network digital twin (NDT) has emerged. This technology combines the capabilities of the digital twin (DT) with communication networks and offers promising solutions for intelligent network management [2]. The significance of DT technology has grown significantly across various domains, aligning with advancements in simulation and computing technology [3].