RelBench: A Benchmark for Deep Learning on Relational Databases Article Swipe
Joshua Robinson
,
Rishabh Ranjan
,
Weihua Hu
,
Kexin Huang
,
Jiaqi Han
,
Alejandro Dobles
,
Matthias Fey
,
Jan Eric Lenssen
,
Yiwen Yuan
,
Zecheng Zhang
,
Xinwei He
,
Jure Leskovec
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.20060
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.20060
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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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2407.20060
- https://arxiv.org/pdf/2407.20060
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401202473
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401202473Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2407.20060Digital Object Identifier
- Title
-
RelBench: A Benchmark for Deep Learning on Relational DatabasesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-29Full publication date if available
- Authors
-
Joshua Robinson, Rishabh Ranjan, Weihua Hu, Kexin Huang, Jiaqi Han, Alejandro Dobles, Matthias Fey, Jan Eric Lenssen, Yiwen Yuan, Zecheng Zhang, Xinwei He, Jure LeskovecList of authors in order
- Landing page
-
https://arxiv.org/abs/2407.20060Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2407.20060Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2407.20060Direct OA link when available
- Concepts
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Benchmark (surveying), Relational database, Computer science, Database, Artificial intelligence, Statistical relational learning, Deep learning, Information retrieval, Data science, Geography, CartographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.paradigm | 97 |
| abstract_inverted_index.provides | 18 |
| abstract_inverted_index.reducing | 139 |
| abstract_inverted_index.research | 168 |
| abstract_inverted_index.spanning | 22 |
| abstract_inverted_index.RelBench, | 2 |
| abstract_inverted_index.RelBench. | 172 |
| abstract_inverted_index.benchmark | 5 |
| abstract_inverted_index.databases | 12, 19 |
| abstract_inverted_index.engineers | 126 |
| abstract_inverted_index.networks. | 16 |
| abstract_inverted_index.research. | 37 |
| abstract_inverted_index.scientist | 124 |
| abstract_inverted_index.End-to-end | 75 |
| abstract_inverted_index.Relational | 48 |
| abstract_inverted_index.databases, | 163 |
| abstract_inverted_index.magnitude. | 149 |
| abstract_inverted_index.predictive | 8, 61, 82, 159 |
| abstract_inverted_index.relational | 11, 162 |
| abstract_inverted_index.thoroughly | 107 |
| abstract_inverted_index.engineering | 101 |
| abstract_inverted_index.experienced | 122 |
| abstract_inverted_index.significant | 91 |
| abstract_inverted_index.demonstrates | 151 |
| abstract_inverted_index.entity-level | 70 |
| abstract_inverted_index.foundational | 33 |
| abstract_inverted_index.comprehensive | 45 |
| abstract_inverted_index.opportunities | 169 |
| abstract_inverted_index.gold-standard, | 113 |
| abstract_inverted_index.infrastructure | 34 |
| abstract_inverted_index.primary-foreign | 86 |
| abstract_inverted_index.representations | 71 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 12 |
| citation_normalized_percentile |