PluRel Dataset

Synthetic Data unlocks Scaling Laws for Relational Foundation Models

Preprocessed synthetic relational databases for pretraining relational foundation models, as introduced in:

PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models Kothapalli, Ranjan, Hudovernik, Dwivedi, Hoffart, Guestrin, Leskovec — arXiv:2602.04029 (2026)

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Data Structure

Each entry is a relbench-compatible Database consisting of multiple relational tables.

Component	Description
Tables	3–20 per database
Primary keys	row_idx (auto-generated)
Foreign keys	foreign_row_0, foreign_row_1, ...
Feature columns	feature_0, feature_1, ... (categorical or numerical)
Time column	date — on activity (leaf) tables only

Schema topology is sampled from: BarabasiAlbert, ReverseRandomTree, or WattsStrogatz graphs.

Data generation uses Structural Causal Models (SCMs) — column dependencies are modeled as DAGs, with values propagated through randomly-initialized MLPs. Activity tables also include trend + cycle + noise time-series.

Parameter	Range
Rows per entity table	500–1,000
Rows per activity table	2,000–5,000
Columns per table	3–40 (power-law)
Missing values	1–10% of numerical columns
Timestamp range	1990–2025
Train / Val / Test	80% / 10% / 10%

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Download

huggingface-cli download kvignesh1420/plurel \
    --repo-type dataset \
    --local-dir ~/scratch/pre

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Usage

Databases are named rel-synthetic-<seed> and are fully reproducible:

from plurel import SyntheticDataset, Config

dataset = SyntheticDataset(seed=42, config=Config())
db = dataset.make_db()

for name, table in db.tables.items():
    print(f"{name}: {table.df.shape}")

See snap-stanford/plurel for installation, configuration, and training scripts.

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Related

Resource	Link
Pretrained checkpoints	kvignesh1420/relational-transformer-plurel
Real-world relbench data	hvag976/relational-transformer

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Citation

@article{kothapalli2026plurel,
  title={{PluRel:} Synthetic Data unlocks Scaling Laws for Relational Foundation Models},
  author={Kothapalli, Vignesh and Ranjan, Rishabh and Hudovernik, Valter and Dwivedi, Vijay Prakash and Hoffart, Johannes and Guestrin, Carlos and Leskovec, Jure},
  journal={arXiv preprint arXiv:2602.04029},
  year={2026}
}