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.

本文研究了大规模通信网络中的信息路由问题，提出了一种基于图神经网络架构的状态扩展策略以最大化源节点的信息聚合，并利用节点的局部信息高效地将所需信息路由到目标节点，通过无监督学习过程将图神经网络输出转化为最优信息路由策略，实验证明该方法在GNN参数化训练方面相比基准算法具有改进的性能。

学习增强状态策略用于通信网络中的信息路由