  Network modeling is a fundamental tool in network research, design, and
operation. Arguably the most popular method for modeling is Queuing Theory
(QT). Its main limitation is that it imposes strong assumptions on the packet
arrival process, which typically do not hold in real networks. In the field of
Deep Learning, Graph Neural Networks (GNN) have emerged as a new technique to
build data-driven models that can learn complex and non-linear behavior. In
this paper, we present \emph{RouteNet-Erlang}, a pioneering GNN architecture
designed to model computer networks. RouteNet-Erlang supports complex traffic
models, multi-queue scheduling policies, routing policies and can provide
accurate estimates in networks not seen in the training phase. We benchmark
RouteNet-Erlang against a state-of-the-art QT model, and our results show that
it outperforms QT in all the network scenarios.
