Graph structure learning

Graph structure learning aims to automatically construct the topology of a graph when graph structure is not available, to achieve semi-supervised node classification. The goal of this task is to utilize limited label information and a large amount of unlabeled data to learn a graph structure that accurately reflects the relationships between nodes. By optimizing the connectivity of the graph and the similarity between nodes, graph structure learning can enhance the performance of node classification, and it is widely applied in social network analysis, recommendation systems, and bioinformatics, among other fields.