RiskNet: Neural Risk Assessment in Networks of Unreliable Resources. (arXiv:2201.12263v1 [cs.NI])

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 …

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