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.
Integrating temporal and structural context in graph transformers for relational deep learning.arXiv preprint arXiv:2511.04557, 2025
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
GelGT proposes collaborative sampling and Gaussian attention on subgraphs to model long-range structural, semantic, and temporal dependencies in relational graphs, reporting up to 13.8% gains on downstream 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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Gaussian Relational Graph Transformer
GelGT proposes collaborative sampling and Gaussian attention on subgraphs to model long-range structural, semantic, and temporal dependencies in relational graphs, reporting up to 13.8% gains on downstream tasks.