Hypergraph Matching

Hypergraph matching aims to find correspondences between nodes in two or more hypergraphs, structures that generalize graphs by allowing edges to connect more than two nodes. Current research focuses on developing efficient algorithms, such as those employing tensor decomposition techniques to reduce computational complexity for large-scale problems, and incorporating uncertainty quantification for improved robustness. These advancements are proving valuable in applications like medical image analysis, where hypergraph matching facilitates tasks such as accurate coronary artery segmentation and improved diagnostic capabilities.