GRDM jointly generates relational database tables via graph-conditional diffusion without table ordering, outperforming autoregressive baselines on multi-hop correlations and single-table fidelity across six real RDBs.
Relational deep learning: Graph representation learning on relational databases
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GNBAN combines heterogeneous graph representation learning with a basis-decomposition head and per-basis attention to achieve 4-5% better WRMSSE on large retail forecasting benchmarks while exposing demand drivers.
Reevaluation of 9 GFMs shows only recent prior-data fitted network models outperform tuned GNNs on node property prediction, at higher cost.
RelPrism generates self-supervised pseudo-tasks from three attribute perspectives via multi-granularity clustering to improve representation learning for relational database prediction tasks.
Introduces curvature-stratified evaluation showing relational learning model rankings are stable within curvature regimes but shift across them, making performance geometry-dependent.
SemStruct models tables as heterogeneous graphs with GNNs on frozen PLM embeddings to incorporate row co-occurrences for schema matching and reports SOTA results on Valentine and SOTAB-SM benchmarks.
A survey proposing a holistic GraphRAG framework with components including query processor, retriever, organizer, generator, and data source, plus domain-tailored reviews, challenges, and future directions.
citing papers explorer
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Joint Relational Database Generation via Graph-Conditional Diffusion Models
GRDM jointly generates relational database tables via graph-conditional diffusion without table ordering, outperforming autoregressive baselines on multi-hop correlations and single-table fidelity across six real RDBs.
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GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets
GNBAN combines heterogeneous graph representation learning with a basis-decomposition head and per-basis attention to achieve 4-5% better WRMSSE on large retail forecasting benchmarks while exposing demand drivers.
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A Fair Evaluation of Graph Foundation Models for Node Property Prediction
Reevaluation of 9 GFMs shows only recent prior-data fitted network models outperform tuned GNNs on node property prediction, at higher cost.
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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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The Post-GCN Decade Revisited: Curvature-Stratified Evaluation of Relational Learning
Introduces curvature-stratified evaluation showing relational learning model rankings are stable within curvature regimes but shift across them, making performance geometry-dependent.
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SemStruct: Contextualizing Semantic Embeddings with Structural Information for Schema Matching
SemStruct models tables as heterogeneous graphs with GNNs on frozen PLM embeddings to incorporate row co-occurrences for schema matching and reports SOTA results on Valentine and SOTAB-SM benchmarks.
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Retrieval-Augmented Generation with Graphs (GraphRAG)
A survey proposing a holistic GraphRAG framework with components including query processor, retriever, organizer, generator, and data source, plus domain-tailored reviews, challenges, and future directions.