Structual Feature Correlation

The task of structure feature relevance prediction aims to leverage the expressive power of Graph Neural Networks (GNNs) to analyze and predict the relationships between different structural features of nodes in a graph. The goal is to uncover hidden structural patterns and dependencies by learning both local and global structural information of the nodes, thereby enhancing the understanding and modeling accuracy of complex network structures. In practical applications, this task holds significant value for social network analysis, bioinformatics, recommendation systems, and other fields, as it can help researchers and engineers more accurately capture key structural features and their mutual influences within networks.