Node Correspondence

Node correspondence, the task of identifying matching nodes across different graphs or networks, is crucial for various applications, including graph similarity computation and multi-network alignment. Current research focuses on developing efficient and robust algorithms, often employing graph neural networks (GNNs) and optimal transport methods, to establish these correspondences, particularly in unsupervised settings where ground truth is unavailable. These advancements improve accuracy and scalability, impacting fields like computer vision (e.g., image registration), bioinformatics (e.g., molecular structure comparison), and social network analysis (e.g., identifying equivalent users across platforms). The development of more expressive and efficient models for node correspondence continues to be a significant area of investigation.