  In many applications, it is important to derive information about the
topology and the internal connections of dynamical systems interacting
together. Examples can be found in fields as diverse as Economics, Neuroscience
and Biochemistry. The paper deals with the problem of deriving a descriptive
model of a network, collecting the node outputs as time series with no use of a
priori insight on the topology, and unveiling an unknown structure as the
estimate of a "sparse Wiener filter". A geometric interpretation of the problem
in a pre-Hilbert space for wide-sense stochastic processes is provided. We cast
the problem as the optimization of a cost function where a set of parameters
are used to operate a trade-off between accuracy and complexity in the final
model. The problem of reducing the complexity is addressed by fixing a certain
degree of sparsity and finding the solution that "better" satisfies the
constraints according to the criterion of approximation. Applications starting
from real data and numerical simulations are provided.
