  Processes on networks consist of two interdependent parts: the network
topology, consisting of the links between nodes, and the dynamics, specified by
some governing equations. This work considers the prediction of the future
dynamics on an unknown network, based on past observations of the dynamics. For
a general class of governing equations, we propose a prediction algorithm which
infers the network as an intermediate step. Inferring the network is impossible
in practice, due to a dramatically ill-conditioned linear system. Surprisingly,
a highly accurate prediction of the dynamics is possible nonetheless: Even
though the inferred network has no topological similarity with the true
network, both networks result in practically the same future dynamics.
