Graph Coarsening

Graph coarsening is a dimensionality reduction technique that simplifies large graphs while preserving key structural properties, primarily to improve the efficiency and scalability of graph-based algorithms. Current research focuses on developing coarsening methods that effectively preserve spectral information, geometric properties, or message-passing guarantees, often employing techniques like spectral clustering, Ricci flow, or optimization-based approaches within various model architectures including Graph Neural Networks (GNNs). These advancements are significant for tackling computationally intensive tasks in diverse fields, such as urban planning (e.g., parking prediction), causal inference, and graph machine learning, where handling massive datasets is crucial.