  This works explores and illustrates recent results developed by the author in
field of dynamical network analysis. The considered approach is blind, i.e., no
a priori assumptions on the interconnected systems are available. Moreover, the
perspective is that of a simple "observer" who can perform no kind of test on
the network in order to study the related response, that is no action or
forcing input aimed to reveal particular responses of the system can be
performed. In such a scenario a frequency based method of investigation is
developed to obtain useful insights on the network. The information thus
derived can be fruitfully exploited to build acyclic graphical models, which
can be seen as extension of Bayesian Networks or Markov Chains. Moreover, it is
shown that the topology of polytree linear networks can be exactly identified
via the same mathematical tools. In this respect, it is worth observing that
important real systems, such as all the transportation networks, fit this
class.
