  We propose a graph neural network (GNN)-based method to predict the
distribution of penalties induced by outages in communication networks, where
connections are protected by resources shared between working and backup paths.
The GNN-based algorithm is trained only with random graphs generated with the
Barab\'asi-Albert model. Even though, the obtained test results show that we
can precisely model the penalties in a wide range of various existing
topologies. GNNs eliminate the need to simulate complex outage scenarios for
the network topologies under study. In practice, the whole design operation is
limited by 4ms on modern hardware. This way, we can gain as much as over 12,000
times in the speed improvement.
