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.
Deep graph structure learning for robust representations: A survey
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LAGA is a unified multi-agent LLM framework that automates comprehensive quality optimization for text-attributed graphs by running detection, planning, action, and evaluation agents in a closed loop.
DEGfM is a dynamic elliptical graph factor model that performs Riemannian optimization on the Grassmann manifold with geodesic temporal regularization to infer time-varying precision matrices.
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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When LLM Agents Meet Graph Optimization: An Automated Data Quality Improvement Approach
LAGA is a unified multi-agent LLM framework that automates comprehensive quality optimization for text-attributed graphs by running detection, planning, action, and evaluation agents in a closed loop.
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Dynamic Elliptical Graph Factor Models via Riemannian Optimization with Geodesic Temporal Regularization
DEGfM is a dynamic elliptical graph factor model that performs Riemannian optimization on the Grassmann manifold with geodesic temporal regularization to infer time-varying precision matrices.