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This paper examines the problem of information routing in a large-scalecommunication network, which can be formulated as a constrained statisticallearning problem having access to only local information. We delineate a novelState Augmentation (SA) strategy to maximize the aggregate information atsource nodes using graph neural network (GNN) architectures, by deploying graphconvolutions over the topological links of the communication network. Theproposed technique leverages only the local information available at each nodeand efficiently routes desired information to the destination nodes. Weleverage an unsupervised learning procedure to convert the output of the GNNarchitecture to optimal information routing strategies. In the experiments, weperform the evaluation on real-time network topologies to validate ouralgorithms. Numerical simulations depict the improved performance of theproposed method in training a GNN parameterization as compared to baselinealgorithms.

Learning State-Augmented Policies for Information Routing in Communication Networks