Graph Sampling

Graph sampling techniques aim to efficiently manage the computational challenges posed by large graphs while preserving crucial structural information. Current research focuses on developing novel sampling algorithms, often integrated with graph neural networks (GNNs), that account for both homophilic and heterophilic graph structures and adapt to specific downstream tasks, such as node classification or PDE solving. These advancements are improving the scalability and accuracy of GNNs and other graph-based methods, impacting diverse fields including fault diagnosis, recommendation systems, and scientific computing.