  Network modeling is a critical component for building self-driving
Software-Defined Networks, particularly to find optimal routing schemes that
meet the goals set by administrators. However, existing modeling techniques do
not meet the requirements to provide accurate estimations of relevant
performance metrics such as delay and jitter. In this paper we propose a novel
Graph Neural Network (GNN) model able to understand the complex relationship
between topology, routing and input traffic to produce accurate estimates of
the per-source/destination pair mean delay and jitter. GNN are tailored to
learn and model information structured as graphs and as a result, our model is
able to generalize over arbitrary topologies, routing schemes and variable
traffic intensity. In the paper we show that our model provides accurate
estimates of delay and jitter (worst case $R^2=0.86$) when testing against
topologies, routing and traffic not seen during training. In addition, we
present the potential of the model for network operation by presenting several
use-cases that show its effective use in per-source/destination pair
delay/jitter routing optimization and its generalization capabilities by
reasoning in topologies and routing schemes not seen during training.
