Node Matching

Node matching, a core problem in graph analysis, aims to identify corresponding nodes across different graphs, enabling tasks like graph similarity computation and network alignment. Current research focuses on improving the efficiency and accuracy of node matching algorithms, often employing graph neural networks (GNNs) to learn node embeddings and incorporating techniques like A* search or regularization to optimize the matching process. These advancements are crucial for various applications, including drug discovery, social network analysis, and image recognition, where efficiently comparing and aligning graph-structured data is essential.