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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AHGCDD distills large hypergraphs into informative synthetic versions via anchor-guided joint optimization and dual-level discrimination, achieving better effectiveness and efficiency than prior decoupled HGC approaches.
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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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Anchor-guided Hypergraph Condensation with Dual-level Discrimination
AHGCDD distills large hypergraphs into informative synthetic versions via anchor-guided joint optimization and dual-level discrimination, achieving better effectiveness and efficiency than prior decoupled HGC approaches.