Graphon Signal

Graphon signal processing aims to analyze signals defined on large graphs by leveraging the mathematical framework of graphons, which represent limits of large graph sequences. Current research focuses on developing efficient sampling techniques for graphon signals, designing and analyzing graphon neural networks (WNNs) for improved transferability and generalization across graphs of varying sizes and topologies, and extending graphon methods to handle sparse graphs, including those with power-law distributions. These advancements offer a powerful tool for analyzing and processing information from large, complex networks, with applications ranging from recommendation systems to multi-agent reinforcement learning.