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We propose a structure-preserving model-reduction methodology for large-scaledynamic networks with tightly-connected components. First, the coherent groupsare identified by a spectral clustering algorithm on the graph Laplacian matrixthat models the network feedback. Then, a reduced network is built, where eachnode represents the aggregate dynamics of each coherent group, and the reducednetwork captures the dynamic coupling between the groups. We provide an upperbound on the approximation error when the network graph is randomly generatedfrom a weight stochastic block model. Finally, numerical experiments align withand validate our theoretical findings.

Learning Coherent Clusters in Weakly-Connected Network Systems