  We consider network topology identification subject to a signal smoothness
prior on the nodal observations. A fast dual-based proximal gradient algorithm
is developed to efficiently tackle a strongly convex, smoothness-regularized
network inverse problem known to yield high-quality graph solutions. Unlike
existing solvers, the novel iterations come with global convergence rate
guarantees and do not require additional step-size tuning. Reproducible
simulated tests demonstrate the effectiveness of the proposed method in
accurately recovering random and real-world graphs, markedly faster than
state-of-the-art alternatives and without incurring an extra computational
burden.
