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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5 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 5representative citing papers
GLMTest integrates code property graphs and GNNs with LLMs to steer test case generation toward targeted branches, raising branch accuracy from 27.4% to 50.2% on the TestGenEval benchmark.
RADAR is a redundancy-aware, query-adaptive framework that uses conditional discrete graph diffusion to generate efficient communication topologies for multi-agent LLM systems, outperforming baselines on six benchmarks with higher accuracy and lower token use.
GS-Quant generates coarse-to-fine discrete codes for KG entities via semantic hierarchy injection and causal sequence reconstruction, enabling LLMs to perform knowledge graph completion by treating the codes as vocabulary tokens.
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
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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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Program Structure-aware Language Models: Targeted Software Testing beyond Textual Semantics
GLMTest integrates code property graphs and GNNs with LLMs to steer test case generation toward targeted branches, raising branch accuracy from 27.4% to 50.2% on the TestGenEval benchmark.
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RADAR: Redundancy-Aware Diffusion for Multi-Agent Communication Structure Generation
RADAR is a redundancy-aware, query-adaptive framework that uses conditional discrete graph diffusion to generate efficient communication topologies for multi-agent LLM systems, outperforming baselines on six benchmarks with higher accuracy and lower token use.
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GS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion
GS-Quant generates coarse-to-fine discrete codes for KG entities via semantic hierarchy injection and causal sequence reconstruction, enabling LLMs to perform knowledge graph completion by treating the codes as vocabulary tokens.
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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.