  In this work, we explore the state-space formulation of network processes to
recover the underlying structure of the network (local connections). To do so,
we employ subspace techniques borrowed from system identification literature
and extend them to the network topology inference problem. This approach
provides a unified view of the traditional network control theory and signal
processing on networks. In addition, it provides theoretical guarantees for the
recovery of the topological structure of a deterministic linear dynamical
system from input-output observations even though the input and state evolution
networks can be different.
