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RelBench: A Benchmark for Deep Learning on Relational Databases

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arxiv 2407.20060 v1 pith:M6Q3NLLE submitted 2024-07-29 cs.LG cs.AIcs.DB

Joshua Robinson , Rishabh Ranjan , Weihua Hu , Kexin Huang , Jiaqi Han , Alejandro Dobles , Matthias Fey , Jan E. Lenssen
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Yiwen Yuan Zecheng Zhang Xinwei He Jure Leskovec
This is my paper · ORCID
classification cs.LGcs.AIcs.DB
keywords modelsrelbenchdatabasesdeeppredictiverelationallearningtasks
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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.

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databases

    cs.DB 2026-02 conditional novelty 6.0 of 10

    A SQL-like domain-specific language that declares predictive tasks on relational databases and automatically generates leakage-free training labels, with batch and low-latency implementations.

  2. Learning Efficient Positional Encodings with Graph Neural Networks

    cs.LG 2025-02 conditional novelty 6.0 of 10

    PEARL generates expressive, stable, and scalable graph positional encodings by passing random or basis node features through message-passing GNNs and pooling the outputs.

  3. A Self-Explainable Heterogeneous GNN for Relational Deep Learning

    cs.LG 2024-11 conditional novelty 6.0 of 10

    MPS-GNN learns predictive meta-paths in relational databases using aggregate statistics over their occurrences, not just existence, and outperforms prior heterogeneous GNNs in experiments.

  4. Rel-HNN: Split Parallel Hypergraph Neural Network for Learning on Relational Databases

    cs.DB 2025-07 reject novelty 5.0 of 10

    Rel-HNN models each tuple as a hyperedge over attribute-value nodes and reports large accuracy gains, but its hypergraph construction appears to include the target label as an input node.

  5. Tackling prediction tasks in relational databases with LLMs

    cs.LG 2024-11 conditional novelty 5.0 of 10

    Pre-trained LLMs, fed serialized relational rows with related examples, achieve competitive AUROC/MAE on RelBench without fine-tuning, but the headline comparison is weakened by pretraining contamination on Formula 1 tasks.

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