  This paper examines the problem of information routing in a large-scale
communication network, which can be formulated as a constrained statistical
learning problem having access to only local information. We delineate a novel
State Augmentation (SA) strategy to maximize the aggregate information at
source nodes using graph neural network (GNN) architectures, by deploying graph
convolutions over the topological links of the communication network. The
proposed technique leverages only the local information available at each node
and efficiently routes desired information to the destination nodes. We
leverage an unsupervised learning procedure to convert the output of the GNN
architecture to optimal information routing strategies. In the experiments, we
perform the evaluation on real-time network topologies to validate our
algorithms. Numerical simulations depict the improved performance of the
proposed method in training a GNN parameterization as compared to baseline
algorithms.
