## 4 GNN-Based Digital Twins for Next-Generation Networks
### Existing Studies
#### 4.2.1 Routing Optimization  
We present the use cases of TwinNet [109], which is a GraphNDT that introduces a digital twin empowered by a GNN model to accurately simulate networks with various queuing configurations, routing, topologies, and traffic models. TwinNet is trained using data generated from a packet-accurate network simulator aiming at producing accurate estimates of per-flow Quality of service (QoS) metrics (e.g., delay, jitter, loss). TwinNet could be coupled with an optimizer to find the routing policies that satisfy complex Service level agreements (SLAs) with increasing traffic intensity. Moreover, the predictability of TwinNet is demonstrated through a what-if scenario involving a budget-constrained network upgrade, showcasing its ability to assess the impact of network upgrades, enabling low-cost trials.  
Accurate modeling of networks is vital to network optimization. Although there exist traditional network models such as queuing theory and fluid model which are extensively used, they often rely on simplifying assumptions and may have limitations in capturing the complexity and dynamics of real-world network systems. Alternatively, packet-level simulators can accurately model networks but come at a high computational cost. In contrast, Deep learning (DL) techniques, particularly GNNs, offer a promising approach that strikes a balance between accuracy and computational cost. GNNs can accurately model network behaviors by being trained on real data without relying on excessive assumptions. Compared to packet-level simulators, GNNs offer a more computationally efficient solution for network modeling. Once trained, GNN-based models can make predictions on network performance in a timely manner.  
TwinNet is trained on the dataset generated from packet-level simulator OMNeT++. Each data sample consists of a simulated network scenario defined by a topology, a source-destination traffic matrix, and a routing and queuing policy. The communication is simulated and the metrics of network performance are obtained to label the corresponding data sample. The traffic matrices are generated to simulate a wide range of network states from low to high traffic loads, including those that may cause congestion and packet loss. Queuing configurations are randomly assigned to each node. The training and evaluation of TwinNet utilize three real-world network topologies. The quantitative results demonstrate the effectiveness of TwinNet, achieving a Mean average percentage error (MAPE) of 3.8%, outperforming baselines that include a multi-layered neural network and a RouteNet [106]. Additionally, TwinNet can be combined with an optimization algorithm for routing optimization. In the paper, experimental results from coupling TwinNet with Direct Search showcase superior performance compared to baselines, which involve the combination of fluid models and the shortest path search. Furthermore, TwinNet enables low-cost trials without impacting the real network. Using TwinNet, the authors can assess the network's capacity enhancement by selecting an additional link to be added. The results demonstrate that the additional link suggested by TwinNet effectively reduces the delay by 40.1% to 54.3%.