  This work examines the problem of topology inference over discrete-time
nonlinear stochastic networked dynamical systems. The goal is to recover the
underlying digraph linking the network agents, from observations of their
state-evolution. The dynamical law governing the state-evolution of the
interacting agents might be nonlinear, i.e., the next state of an agent can
depend nonlinearly on its current state and on the states of its immediate
neighbors. We establish sufficient conditions that allow consistent graph
learning over a special class of networked systems, namely, logistic-type
dynamical systems.
