  Dynamical processes on complex networks such as information propagation,
innovation diffusion, cascading failures or epidemic spreading are highly
affected by their underlying topologies as characterized by, for instance,
degree-degree correlations. Here, we introduce the concept of copulas in order
to artificially generate random networks with an arbitrary degree distribution
and a rich a priori degree-degree correlation (or `association') structure. The
accuracy of the proposed formalism and corresponding algorithm is numerically
confirmed. The derived network ensembles can be systematically deployed as
proper null models, in order to unfold the complex interplay between the
topology of real networks and the dynamics on top of them.
