  We consider a networked linear dynamical system with $p$ agents/nodes. We
study the problem of learning the underlying graph of interactions/dependencies
from observations of the nodal trajectories over a time-interval $T$. We
present a regularized non-casual consistent estimator for this problem and
analyze its sample complexity over two regimes: (a) where the interval $T$
consists of $n$ i.i.d. observation windows of length $T/n$ (restart and
record), and (b) where $T$ is one continuous observation window (consecutive).
Using the theory of $M$-estimators, we show that the estimator recovers the
underlying interactions, in either regime, in a time-interval that is
logarithmic in the system size $p$. To the best of our knowledge, this is the
first work to analyze the sample complexity of learning linear dynamical
systems \emph{driven by unobserved not-white wide-sense stationary (WSS)
inputs}.
