Graph Signal Adaptive Message Passing

This paper proposes Graph Signal Adaptive Message Passing (GSAMP) by employing a distinct approach that utilizes localized computations at each node based on an adaptive solution obtained from an optimization problem designed to minimize the discrepancy between observed and estimated values. This localized approach distinguishes GSAMP from conventional graph methods derived from the entire graph. GSAMP efficiently handles real-world time-varying graph signals under Gaussian and impulsive noise, performing online prediction, missing data imputation, and noise removal simultaneously.