  Recent advances in Machine Learning (ML) have shown a great potential to
build data-driven solutions for a plethora of network-related problems. In this
context, building fast and accurate network models is essential to achieve
functional optimization tools for networking. However, state-of-the-art
ML-based techniques for network modelling are not able to provide accurate
estimates of important performance metrics such as delay or jitter in realistic
network scenarios with sophisticated queue scheduling configurations. This
paper presents a new Graph-based deep learning model able to estimate
accurately the per-path mean delay in networks. The proposed model can
generalize successfully over topologies, routing configurations, queue
scheduling policies and traffic matrices unseen during the training phase.
