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.

翻译：本文研究了大规模通信网络中的信息路由问题，该问题可建模为一个仅能获取局部信息的约束统计学习问题。我们提出了一种新颖的状态增强（State Augmentation, SA）策略，通过在图神经网络架构上部署基于通信网络拓扑链接的图卷积操作，以最大化源节点的聚合信息量。该技术仅利用每个节点可获取的局部信息，即可高效地将所需信息路由至目标节点。我们采用无监督学习流程，将图神经网络架构的输出转化为最优信息路由策略。在实验中，我们基于实时网络拓扑进行性能评估以验证算法有效性。数值仿真表明，相较于基线算法，所提方法在训练图神经网络参数化时展现出更优性能。