Stationary Graph Process

Stationary graph processes model data on irregular networks, aiming to capture correlations across both nodes and time. Current research focuses on extending these models to handle locally varying characteristics within a graph, employing techniques like locally stationary graph processes and subgraph stationary optimizations for improved efficiency. These advancements are significant for signal processing and machine learning applications, enabling more accurate signal interpolation and faster, more energy-efficient inference on graph-structured data. The development of algorithms for learning these processes from incomplete data is a key area of ongoing investigation.