  Forecasting the dynamics of large complex networks from previous time-series
data is important in a wide range of contexts. Here we present a machine
learning scheme for this task using a parallel architecture that mimics the
topology of the network of interest. We demonstrate the utility and scalability
of our method implemented using reservoir computing on a chaotic network of
oscillators. Two levels of prior knowledge are considered: (i) the network
links are known; and (ii) the network links are unknown and inferred via a
data-driven approach to approximately optimize prediction.
