RelBench: A Benchmark for Deep Learning on Relational Databases

TL;DR

RelBench introduces a comprehensive benchmark for evaluating deep learning models on relational databases, demonstrating that end-to-end learned relational deep learning models outperform manual feature engineering while significantly reducing human effort.

Contribution

The paper presents RelBench, a new benchmark for relational deep learning, and provides the first extensive study showing deep models outperform manual feature engineering in this domain.

Findings

RDL models outperform manual feature engineering in predictive tasks.

End-to-end RDL reduces human effort by over tenfold.

RelBench enables future research in deep learning for relational databases.

Abstract

We present RelBench, a public benchmark for solving predictive tasks over relational databases with graph neural networks. RelBench provides databases and tasks spanning diverse domains and scales, and is intended to be a foundational infrastructure for future research. We use RelBench to conduct the first comprehensive study of Relational Deep Learning (RDL) (Fey et al., 2024), which combines graph neural network predictive models with (deep) tabular models that extract initial entity-level representations from raw tables. End-to-end learned RDL models fully exploit the predictive signal encoded in primary-foreign key links, marking a significant shift away from the dominant paradigm of manual feature engineering combined with tabular models. To thoroughly evaluate RDL against this prior gold-standard, we conduct an in-depth user study where an experienced data scientist manually engineers features for each task. In this study, RDL learns better models whilst reducing human work needed by more than an order of magnitude. This demonstrates the power of deep learning for solving predictive tasks over relational databases, opening up many new research opportunities enabled by RelBench.