  Signal processing and machine learning algorithms for data supported over
graphs, require the knowledge of the graph topology. Unless this information is
given by the physics of the problem (e.g., water supply networks, power grids),
the topology has to be learned from data. Topology identification is a
challenging task, as the problem is often ill-posed, and becomes even harder
when the graph structure is time-varying. In this paper, we address the problem
of dynamic topology identification by building on recent results from
time-varying optimization, devising a general-purpose online algorithm
operating in non-stationary environments. Because of its iteration-constrained
nature, the proposed approach exhibits an intrinsic temporal-regularization of
the graph topology without explicitly enforcing it. As a case-study, we
specialize our method to the Gaussian graphical model (GGM) problem and
corroborate its performance.
