  Graph neural networks (GNN) have shown outstanding applications in many
fields where data is fundamentally represented as graphs (e.g., chemistry,
biology, recommendation systems). In this vein, communication networks comprise
many fundamental components that are naturally represented in a
graph-structured manner (e.g., topology, configurations, traffic flows). This
position article presents GNNs as a fundamental tool for modeling, control and
management of communication networks. GNNs represent a new generation of
data-driven models that can accurately learn and reproduce the complex
behaviors behind real networks. As a result, such models can be applied to a
wide variety of networking use cases, such as planning, online optimization, or
troubleshooting. The main advantage of GNNs over traditional neural networks
lies in its unprecedented generalization capabilities when applied to other
networks and configurations unseen during training, which is a critical feature
for achieving practical data-driven solutions for networking. This article
comprises a brief tutorial on GNNs and their possible applications to
communication networks. To showcase the potential of this technology, we
present two use cases with state-of-the-art GNN models respectively applied to
wired and wireless networks. Lastly, we delve into the key open challenges and
opportunities yet to be explored in this novel research area.
