FROG makes full-resolution graph structure learnable in relational deep learning by modeling table roles as optimizable components in message passing, regularized by functional dependency constraints.
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RelBench v2 expands a relational deep learning benchmark with four new large datasets and autocomplete tasks, showing models that use table relationships outperform single-table baselines.
RelPrism generates self-supervised pseudo-tasks from three attribute perspectives via multi-granularity clustering to improve representation learning for relational database prediction tasks.
citing papers explorer
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Is Fixing Schema Graphs Necessary? Full-Resolution Graph Structure Learning for Relational Deep Learning
FROG makes full-resolution graph structure learnable in relational deep learning by modeling table roles as optimizable components in message passing, regularized by functional dependency constraints.
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RelBench v2: A Large-Scale Benchmark and Repository for Relational Data
RelBench v2 expands a relational deep learning benchmark with four new large datasets and autocomplete tasks, showing models that use table relationships outperform single-table baselines.
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RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases
RelPrism generates self-supervised pseudo-tasks from three attribute perspectives via multi-granularity clustering to improve representation learning for relational database prediction tasks.
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