RelBench: A Benchmark for Deep Learning on Relational Databases
This provides a foundational infrastructure for future research in deep learning on relational databases, addressing the problem of manual feature engineering for data scientists.
The authors tackled the problem of predictive tasks over relational databases by introducing RelBench, a benchmark for evaluating graph neural networks, and demonstrated that their Relational Deep Learning (RDL) approach learns better models while reducing human work by more than an order of magnitude compared to manual feature engineering.
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.