Novel method of reconstructing the topology of dynamical networks from time series is proposed. By examining the variable--derivative correlation of the network node pairs, we derive a simple equation yielding the network adjacency matrix. Our key assumption is that the intra-network interaction functions are known. We illustrate the method on a simple example, and discuss the dependence of the reconstruction on the dynamical properties of time series. Our method is applicable to any weighted or directed network, in principle allowing for precision to be estimated.
Reconstructing complex networks from time series