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.
Artificial Intelligence Review , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
FOCAL fuses unconstrained coverage attention and meta-path anchoring attention to improve multi-label classification on heterogeneous graphs by resolving semantic dilution versus coverage constraint trade-offs.
citing papers explorer
-
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.
-
FOCAL-Attention for Heterogeneous Multi-Label Prediction
FOCAL fuses unconstrained coverage attention and meta-path anchoring attention to improve multi-label classification on heterogeneous graphs by resolving semantic dilution versus coverage constraint trade-offs.