Graph Pruning

Graph pruning aims to reduce the size and complexity of graphs while preserving essential information, thereby improving the efficiency and performance of graph-based algorithms, particularly graph neural networks (GNNs). Current research focuses on developing effective pruning strategies, often employing machine learning models to identify and remove less important nodes or edges, with approaches ranging from self-supervised learning to methods leveraging graph signal processing and locality-sensitive hashing. These advancements are significant because they enable the application of GNNs to larger, more complex datasets and improve the interpretability and speed of various applications, including recommendation systems, disease prediction, and sustainable development planning.