  A method of network reconstruction from the dynamical time series is
introduced, relying on the concept of derivative-variable correlation. Using a
tunable observable as a parameter, the reconstruction of any network with known
interaction functions is formulated via simple matrix equation. We suggest a
procedure aimed at optimizing the reconstruction from the time series of length
comparable to the characteristic dynamical time scale. Our method also provides
a reliable precision estimate. We illustrate the method's implementation via
elementary dynamical models, and demonstrate its robustness to both model and
observation errors.
