Set-to-Graph Prediction

Set-to-Graph Prediction is a machine learning task that involves converting set data into graph structures, aiming to learn the relationships between elements within the set and generate graphs with nodes and edges. The goal of this task is to capture complex interaction patterns within the set, construct accurate graph representations, and support downstream tasks such as graph classification and link prediction. Its application value lies in effectively processing and analyzing unstructured data, enhancing model performance in areas like social networks, molecular structures, recommendation systems, and more.