Expander Graph

Expander graphs, highly connected sparse graphs with small diameters, are increasingly used to improve information flow in graph-based machine learning models, particularly addressing the "oversquashing" problem where long-range dependencies are lost. Current research focuses on incorporating expander graphs into graph neural networks (GNNs) through various propagation methods and rewiring algorithms, often leveraging Cayley graphs or constructing expander hierarchies for efficient computation. This work is significant because it enhances the performance and scalability of GNNs for tasks like graph classification and clustering, and also finds applications in decentralized federated learning by creating robust and efficient communication networks.