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In many applications, it is important to derive information about thetopology and the internal connections of dynamical systems interactingtogether. Examples can be found in fields as diverse as Economics, Neuroscienceand Biochemistry. The paper deals with the problem of deriving a descriptivemodel of a network, collecting the node outputs as time series with no use of apriori insight on the topology, and unveiling an unknown structure as theestimate of a "sparse Wiener filter". A geometric interpretation of the problemin a pre-Hilbert space for wide-sense stochastic processes is provided. We castthe problem as the optimization of a cost function where a set of parametersare used to operate a trade-off between accuracy and complexity in the finalmodel. The problem of reducing the complexity is addressed by fixing a certaindegree of sparsity and finding the solution that "better" satisfies theconstraints according to the criterion of approximation. Applications startingfrom real data and numerical simulations are provided.

Model Identification of a Network as Compressing Sensing