  We propose a structure-preserving model-reduction methodology for large-scale
dynamic networks with tightly-connected components. First, the coherent groups
are identified by a spectral clustering algorithm on the graph Laplacian matrix
that models the network feedback. Then, a reduced network is built, where each
node represents the aggregate dynamics of each coherent group, and the reduced
network captures the dynamic coupling between the groups. We provide an upper
bound on the approximation error when the network graph is randomly generated
from a weight stochastic block model. Finally, numerical experiments align with
and validate our theoretical findings.
