  We propose a novel 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. Our approach is
theoretically justified under a random graph setting. Finally, numerical
experiments align with and validate our theoretical findings.
