The complexity of the dynamic behaviour in non-linear systems depends on the number of state variables involved and on how they are connected. In recent times the description of chaotic behaviour and of strange attractors have been the subject of numerous investigations. Most methods developed for the characterization of dynamic systems are based on the phase space representation. In the approach presented here, however, a system's behaviour is described in terms of the network comprising the interactions among the state variables. Information theoretical methods are introduced to quantify a network's organization along the dimensions of time and of the sign of the interactions. Examples are provided by applying the information measures to a simple network model and to the interaction network derived for the Lorenz equations.