A research team from Kumamoto University has developed a promising deep learning model that significantly enhances the accuracy of subgraph matching—a critical task in fields ranging from drug ...

However, conventional Graph Neural Networks (GNNs) often struggle with accuracy when "extra" or irrelevant nodes in the data interfere with the matching process.