FROG

FROG: Full-Resolution Graph Structure Learning for Relational Deep Learning

This paper introduces FROG, a framework that treats relational database schema graph construction as a learnable optimization problem rather than a fixed design choice. The method formulates table role modeling as a learnable component, allowing tables to dynamically serve as nodes or edges in message passing, with functional dependency constraints ensuring semantic consistency. Experiments show FROG outperforms existing relational deep learning approaches and provides insights into how table roles affect downstream prediction tasks.