Graph Signal

Graph signal processing (GSP) focuses on analyzing data residing on graph structures, aiming to leverage the inherent relationships between data points for improved signal processing tasks. Current research emphasizes developing robust algorithms for graph learning (inferring the graph structure from data) and signal reconstruction (handling missing or noisy data), often employing graph neural networks (GNNs), adaptive filters, and optimization techniques like ADMM. These advancements are impacting diverse fields, including sensor networks, time-series forecasting, and anomaly detection, by enabling more accurate and efficient analysis of complex, interconnected data.