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By collecting and expanding several numerical recipes developed in previouswork, we implement an object-oriented Python code, based on the networkXlibrary, for the realization of the configuration model and Newman rewiring.The software can be applied to any kind of network and "target" correlations,but it is tested with focus on scale-free networks and assortativecorrelations. For generating the degree sequence we use the method of "randomhubs", which gives networks with minimal fluctuations. For the assortativerewiring we use the simple Vazquez-Weigt matrix as a test in the case of randomnetworks; since it appears to be not effective in the case of scale-freenetworks we then turn to another recipe which generates matrices withdecreasing off-diagonal elements. The rewiring procedure is important also atthe theoretical level, in order to test which types of statistically acceptablecorrelations can actually be realized in concrete networks. From theapplicative point of view, its main use is in the construction of correlatednetworks for the solution of dynamical or diffusion processes by analyzing theevolution of single nodes, i.e., beyond the Heterogeneous Mean Fieldapproximation. As an example, we report on an application to the Bass diffusionmodel, with calculations of the time $t_{max}$ of the diffusion peak. The samenetworks can additionally be exported in environments for agent-basedsimulations like NetLogo.

Generation of scale-free assortative networks via Newman rewiring for simulation of diffusion phenomena