  In order to promote the use of machine learning in 5G, the International
Telecommunication Union (ITU) proposed in 2021 the second edition of the ITU
AI/ML in 5G challenge, with over 1600 participants from 82 countries. This work
details the second place solution overall, which is also the winning solution
of the Graph Neural Networking Challenge 2021. We tackle the problem of
generalization when applying a model to a 5G network that may have longer paths
and larger link capacities than the ones observed in training. To achieve this,
we propose to first extract robust features related to Queueing Theory (QT),
and then fine-tune the analytical baseline prediction using a modification of
the Routenet Graph Neural Network (GNN) model. The proposed solution
generalizes much better than simply using Routenet, and manages to reduce the
analytical baseline's 10.42 mean absolute percent error to 1.45 (1.27 with an
ensemble). This suggests that making small changes to an approximate model that
is known to be robust can be an effective way to improve accuracy without
compromising generalization.
