Graph Regression

Graph Regression is a graph learning task aimed at solving regression problems by predicting continuous-valued attributes of graphs. Unlike graph classification tasks, Graph Regression uses different loss functions and performance evaluation metrics to more accurately capture the complex relationships and continuous variations within graph structures. This task holds significant application value in areas such as drug discovery, materials science, and social network analysis, effectively supporting deeper understanding and prediction of graph data.