This article studies reduced-order modeling of dynamic networks with strongly connected topology. Given a graph clustering of an original large-scale network, we construct a quotient graph with less number of vertices, where the edge weights are parameters to be determined. The model of a reduced network is thereby obtained with parameterized system matrices, and then, an edge weighting procedure is devised, aiming to select an optimal set of edge weights to minimize the approximation error between the original and the reduced-order network models in terms of $\mathcal {H}_{2}$ -norm. The effectiveness of the proposed method is illustrated by a numerical example.

中文翻译：

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扩散耦合网络系统的降阶建模：最优边缘加权方法

本文研究具有强连接拓扑的动态网络的降阶建模。给定原始大规模网络的图聚类，我们构造一个顶点数量较少的商图，其中边权重是待确定的参数。由此，通过参数化系统矩阵获得简化网络模型，然后设计边缘加权程序，旨在选择一组最佳边缘权重，以最小化原始网络模型和降阶网络模型之间的近似误差的$\mathcal {H}_{2}$ -规范。通过数值例子说明了该方法的有效性。