Recent work on linear control models for complex systems has examined their controllability 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. In the study of effective connectivity in the brain, 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. Motivated by this, 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 in neuroscience to determine effective connectivity in the brain while performing cognitive tasks, we study the problem of identifying linear control networks from input-output data when there are latent nodes whose presence and number is unknown. We examine to what extent the transfer function of the manifest subnetwork can be reconstructed and explore the design of procedures to determine whether interactions between manifest nodes are direct or mediated by latent nodes.