  Network digital twins (NDTs) facilitate the estimation of key performance
indicators (KPIs) before physically implementing a network, thereby enabling
efficient optimization of the network configuration. In this paper, we propose
a learning-based NDT for network simulators. The proposed method offers a
holistic representation of information flow in a wireless network by
integrating node, edge, and path embeddings. Through this approach, the model
is trained to map the network configuration to KPIs in a single forward pass.
Hence, it offers a more efficient alternative to traditional simulation-based
methods, thus allowing for rapid experimentation and optimization. Our proposed
method has been extensively tested through comprehensive experimentation in
various scenarios, including wired and wireless networks. Results show that it
outperforms baseline learning models in terms of accuracy and robustness.
Moreover, our approach achieves comparable performance to simulators but with
significantly higher computational efficiency.
