Is Fixing Schema Graphs Necessary? Full-Resolution Graph Structure Learning for Relational Deep Learning

TL;DR

This paper introduces FROG, a framework for learning graph structures in relational deep learning that jointly optimizes graph construction and GNN representations, outperforming fixed-structure methods.

Contribution

FROG formulates relational structure learning as a learnable role modeling problem, enabling adaptive graph construction and improved relational prediction performance.

Findings

FROG outperforms existing fixed-structure methods in relational tasks.

Role-driven message passing captures relational semantics effectively.

Regularization with functional dependency constraints maintains semantic consistency.

Abstract

Relational prediction tasks are fundamental in many real-world applications, where data are naturally stored in relational databases (RDBs). Relational Deep Learning (RDL) addresses this problem by modeling RDBs as graphs and applying graph neural networks (GNNs) for end-to-end learning. However, the full-resolution property is commonly adopted as a design principle in graph construction for RDBs to preserve relational semantics, which leads most existing methods to rely on fixed graph structures. In this paper, we propose FROG, a Full-Resolution and Optimizable Graph Structure Learning} framework for RDL that formulates relational structure learning as a learnable table role modeling problem, allowing tables to contribute as nodes and edges in message passing. We further design role-driven message passing mechanisms to capture relational semantics, enabling joint optimization of graph structure and GNN representations. To ensure semantic consistency, we introduce functional dependency constraints that regularize representations across table and entity levels. Extensive experiments demonstrate that our method outperforms existing approaches and reveal how table roles impact downstream tasks, offering new insights into graph construction for RDL