  We consider a class of stochastic dynamical networks whose governing dynamics
can be modeled using a coupling function. It is shown that the dynamics of such
networks can generate geometrically ergodic trajectories under some reasonable
assumptions. We show that a general class of coupling functions can be learned
using only one sample trajectory from the network. This is practically
plausible as in numerous applications it is desired to run an experiment only
once but for a longer period of time, rather than repeating the same experiment
multiple times from different initial conditions. Building upon ideas from the
concentration inequalities for geometrically ergodic Markov chains, we
formulate several results about the convergence of the empirical estimator to
the true coupling function. Our theoretical findings are supported by extensive
simulation results.
