RiskNet: Neural Risk Assessment in Networks of Unreliable Resources
This provides a fast neural risk assessment tool for network operators dealing with unreliable resources, though it is incremental as it applies GNNs to a specific domain problem.
The authors tackled the problem of predicting penalty distributions from outages in communication networks with shared backup resources, achieving a 12,000x speed improvement over simulations while maintaining accuracy across various topologies.
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 Barabási-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.