Network Alignment

Network alignment aims to identify corresponding nodes across different networks, a crucial task with applications ranging from image segmentation to program analysis. Current research focuses on improving the accuracy and scalability of alignment algorithms, employing techniques like graph neural networks, optimal transport methods, and multi-level frameworks to handle large, complex networks and address the computational challenges inherent in the problem. These advancements are driving progress in diverse fields, enabling more effective cross-network data integration and analysis for tasks such as identifying similar individuals across social media platforms or comparing protein-protein interaction networks.