Reaction-diffusion clustering of single-input dynamical networks

A novel clustering method for single-input dynamical networks is proposed, where we aggregate state variables that behave similarly for any input signals. This clustering method is based on the Reaction-Diffusion transformation, which can be applied to large-scale networks, and preserves the stability as well as a kind of network structure of the original system. In addition, the upper bound of the state discrepancy caused by the clustering is evaluated in terms of H∞-norm.