This paper studies a model reduction method for linear consensus networks consisting of diffusively coupled single-integrators. For a given graph clustering of an original complex network, we construct a simplified network consisting of fewer nodes, where the edge weights are to be determined. An optimal weight assignment procedure is proposed to select suitable edge weights of the reduced network, aiming for the minimum H2 approximation error between the original network and the reduced-order network model. The effectiveness of the proposed method is illustrated by means of an example.

This paper studies reduced-order modeling of dynamic networks with strongly connected topology. Given a graph clustering of an original complex network, we construct a quotient graph with less number of vertices, where the edge weights are parameters to be determined. The model of the 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 that minimizes the approximation error between the original and the reduced-order network models in terms of H2-norm. The effectiveness of the proposed method is illustrated by a numerical example.

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