What Makes a Desired Graph for Relational Deep Learning?

2026-06-07

AI Summary

This work addresses the challenge that graph structures naively derived from relational databases often suffer from information overload and semantic fragmentation, rendering them ill-suited for relational reasoning with graph neural networks (GNNs). To overcome this limitation, the authors propose an end-to-end structure optimizer that automatically constructs GNN-friendly relational graphs by jointly performing information filtering and semantic enrichment. The method uncovers key mechanisms underlying effective adaptation of relational graphs to GNN architectures. Empirical evaluation across 26 diverse tasks—including classification, regression, and recommendation—demonstrates consistent improvements in model accuracy, frequently accompanied by reduced inference overhead.

Abstract

Relational deep learning (RDL) converts relational databases (RDBs) into heterogeneous graphs, but graphs derived directly from database schemas are often not well suited for how graph neural networks (GNNs) perform relational reasoning. We study what makes a relational graph suitable for deep learning and show that schema-derived graphs suffer from two systematic failures: information overload and semantic fragmentation. Our empirical analysis reveals that the desired graph is not the raw schema, but a result of controlled structural adaptation. Performance depends on balancing two operations: mitigating information overload via filtering, and repairing semantic fragmentation via injection. Specifically, filtering serves as a bias-variance knob with non-monotonic effects, while injection improves performance only when it explicitly restores the relational dependencies missing from the original schema. Based on these findings, we develop an end-to-end structural optimizer that applies both operations to adapt relational graphs automatically. Across 26 tasks spanning classification, regression, and recommendation, the optimized graphs consistently improve accuracy while often reducing inference cost.

Problem

• relational deep learning
• heterogeneous graphs
• information overload
• semantic fragmentation
• graph neural networks

Innovation

• relational deep learning
• graph neural networks
• structural optimization
• information overload
• semantic fragmentation