So far, most of the reduction techniques for networks of systems rely on clustering, because they easily preserve the network structure in the reduced model. Application of the classical methods that are very relevant from a control systems perspective, such as balancing and moment matching based model reduction, generally do not preserve the network structure. In this presentation we focus on the generalisation of these classical methods in such a way that both the amount of nodes and the dynamics of the nodes are reduced, while preserving relevant network and dynamics structures. The developments are done for networks of linear systems, as well as networks with Lur’e dynamics on the nodes. Furthermore, a priori error bounds will be provided, and relevant small and large scale examples will be used to illustrate the results.