Accelerated Graph Learning From Smooth Signals

• Published in: IEEE signal processing letters Vol. 28; pp. 2192 - 2196
• Main Authors: Saboksayr, Seyed Saman, Mateos, Gonzalo
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Convergence; Convex functions; fast gradient methods; Gradient methods; Graph learning; graph signal processing; Graph theory; Inference algorithms; Inverse problems; Network topologies; Network topology; Signal processing algorithms; signal smoothness; Smoothness; topology identification
• ISSN: 1070-9908; 1558-2361
• DOI: 10.1109/LSP.2021.3123459

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.