Graph Alignment

Graph alignment aims to identify corresponding nodes across multiple graphs, leveraging both structural and feature information to establish a one-to-one mapping. Current research focuses on developing unsupervised and semi-supervised methods, employing techniques like optimal transport (particularly Gromov-Wasserstein distance), graph neural networks, and embedding-based approaches often combined with structure learning or alignment-based regularization. These advancements improve accuracy and robustness in various applications, including knowledge graph alignment, recommendation systems, and chemical synthesis design, by enabling more effective integration and analysis of interconnected data.