In this paper, we review the concept of graph neural network models, network of digital twin applications, and their comparison with other different fields.

Graph Neural Networks (GNNs) have emerged as a powerful framework for analyzing and extracting information from complex network data.

In this paper, we review the concept of graph neural network models, network of digital twin applications, and their comparison with other different fields.

Apr 15, 2023 · This draft introduces the scenarios and requirements for performance modeling of digital twin networks, and explores the implementation ...

Apr 20, 2023 · 1) We propose Digital Twin Graph (DTG), a general graph representation of interconnected physical systems, which re- quires no domain knowledge ...

Nov 9, 2022 · In this paper, we propose a GNN-based network model able to understand the complex relationship between the queueing policy (scheduling algorithm and queue ...

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.

Missing: Conceptual Framework.

This paper proposes an innovative solution to the challenge of positioning pressure sensors in a water distribution network. It is important to highlight that ...

This survey explores the intersection of GNNs and Network digital twins (NDTs), providing an overview of their applications, enabling technologies, challenges, ...