Recent work on linear control models for complex systems has examined their controllability properties and specifically explored the characterization of the ease (in terms of required energy) with which they can be controlled by means of a finite number of actuators, each affecting an individual node. Motivated by the study of effective connectivity in the brain, where external inputs not only have direct effect on the state of the brain in a particular area, but can also activate the connections among different brain areas, in the first part of the talk we study controllability metrics for bilinear control models of complex systems, where inputs might not only affect the state of a node, but also their interconnection. In the second part of the talk, motivated by the identification procedures used by neuroscientists to determine the effective connectivity in the brain, we examine the problem of identifying the structure of a linear control network from input-output data when there are latent nodes whose presence is unknown.