  We address the issue of estimating the topology and dynamics of sparse linear
dynamic networks in a hyperparameter-free setting. We propose a method to
estimate the network dynamics in a computationally efficient and parameter
tuning-free iterative framework known as SPICE (Sparse Iterative Covariance
Estimation). The estimated dynamics directly reveal the underlying topology.
Our approach does not assume that the network is undirected and is applicable
even with varying noise levels across the modules of the network. We also do
not assume any explicit prior knowledge on the network dynamics. Numerical
experiments with realistic dynamic networks illustrate the usefulness of our
method.
