RAM augments relational graph models with attribute-semantic retrieval via random-walk documents and two contrastive augmentations (ATRA, ETRA) to achieve state-of-the-art results on five real-world databases.
Jakub Peleška and Gustav Šír
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
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.
RelGT-AC adds column masking, unified task head, and TF-IDF encoding to RelGT, outperforming GraphSAGE on regression autocomplete tasks and gaining up to 10 AUROC on text-heavy tasks across RelBench v2 datasets.
citing papers explorer
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From Schema to Signal: Retrieval-Augmented Modeling for Relational Data Analytics
RAM augments relational graph models with attribute-semantic retrieval via random-walk documents and two contrastive augmentations (ATRA, ETRA) to achieve state-of-the-art results on five real-world databases.
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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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RelGT-AC: A Relational Graph Transformer for Autocomplete Tasks in Relational Databases
RelGT-AC adds column masking, unified task head, and TF-IDF encoding to RelGT, outperforming GraphSAGE on regression autocomplete tasks and gaining up to 10 AUROC on text-heavy tasks across RelBench v2 datasets.