  Topology inference for networked dynamical systems (NDSs) has received
considerable attention in recent years. The majority of pioneering works have
dealt with inferring the topology from abundant observations of NDSs, so as to
approximate the real one asymptotically. Leveraging the characteristic that
NDSs will react to various disturbances and the disturbance's influence will
consistently spread, this paper focuses on inferring the topology by a few
active excitations. The key challenge is to distinguish different influences of
system noises and excitations from the exhibited state deviations, where the
influences will decay with time and the exciatation cannot be arbitrarily
large. To practice, we propose a one-shot excitation based inference method to
infer $h$-hop neighbors of a node. The excitation conditions for accurate
one-hop neighbor inference are first derived with probability guarantees. Then,
we extend the results to $h$-hop neighbor inference and multiple excitations
cases, providing the explicit relationships between the inference accuracy and
excitation magnitude. Specifically, the excitation based inference method is
not only suitable for scenarios where abundant observations are unavailable,
but also can be leveraged as auxiliary means to improve the accuracy of
existing methods. Simulations are conducted to verify the analytical results.
