Graph Matching

Graph matching aims to find the optimal correspondence between nodes in two or more graphs, a fundamental problem with applications across diverse fields like computer vision, bioinformatics, and data valuation. Current research emphasizes developing robust and efficient algorithms, often employing graph neural networks, optimal transport methods, and integer programming formulations to address challenges such as noisy data, partial matches, and large-scale graphs. These advancements are improving accuracy and scalability in applications ranging from 3D object recognition and scene alignment to cross-domain knowledge transfer and privacy-preserving model fusion.