Graph Partitioning

Graph partitioning aims to divide a graph's nodes into clusters with dense internal connections and sparse inter-cluster links, optimizing various objectives like minimizing cut size or maximizing modularity. Current research emphasizes developing efficient algorithms, often incorporating graph neural networks (GNNs) and pre-training techniques, to handle increasingly large graphs and diverse application needs, including unsupervised segmentation and device placement optimization. These advancements are crucial for improving the scalability and performance of GNNs and other graph-based methods across numerous fields, from computer-assisted surgery to large-scale traffic control and knowledge graph management.