  The paper presents a solution to the problem of universal representation of
graphs exemplifying communication network topologies with the help of neural
networks. The proposed approach is based on message-passing neural networks
(MPNN). The approach enables us to represent topologies and operational aspects
of networks. The usefulness of the solution is illustrated with a case study of
delay prediction in queuing networks. This shows that performance evaluation
can be provided without having to apply complex modeling. In consequence, the
proposed solution makes it possible to effectively apply methods elaborated in
the field of machine learning in communications.
