GTLM injects graph-aware attention biases into LLMs using only 0.015% extra parameters, enabling native graph processing that matches 7B models with a 1B model on text-attributed graph benchmarks.
Advances in neural information processing systems , volume=
6 Pith papers cite this work. Polarity classification is still indexing.
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2026 6verdicts
UNVERDICTED 6representative citing papers
A dual-purpose benchmark supplies two text-derived knowledge graphs and one expert reference graph on the same biomedical corpus to jointly measure construction method quality and GNN robustness via semi-supervised node classification.
DuConTE is a dual-granularity text encoder that incorporates graph topology into language model attention for improved node representations in text-attributed graphs.
The survey groups attention-based GNNs into three stages—graph recurrent attention networks, graph attention networks, and graph transformers—while reviewing architectures and future directions.
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.
A novel CVAE with mixture scheduling achieves fine-grained structural control in graph generation, showing high quality and controllability on five datasets.
citing papers explorer
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Teaching LLMs to See Graphs: Unifying Text and Structural Reasoning
GTLM injects graph-aware attention biases into LLMs using only 0.015% extra parameters, enabling native graph processing that matches 7B models with a 1B model on text-attributed graph benchmarks.
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A Unified Benchmark for Evaluating Knowledge Graph Construction Methods and Graph Neural Networks
A dual-purpose benchmark supplies two text-derived knowledge graphs and one expert reference graph on the same biomedical corpus to jointly measure construction method quality and GNN robustness via semi-supervised node classification.
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DuConTE: Dual-Granularity Text Encoder with Topology-Constrained Attention for Text-attributed Graphs
DuConTE is a dual-granularity text encoder that incorporates graph topology into language model attention for improved node representations in text-attributed graphs.
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Attention-based graph neural networks: a survey
The survey groups attention-based GNNs into three stages—graph recurrent attention networks, graph attention networks, and graph transformers—while reviewing architectures and future directions.
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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.
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Fine-Grained Graph Generation through Latent Mixture Scheduling
A novel CVAE with mixture scheduling achieves fine-grained structural control in graph generation, showing high quality and controllability on five datasets.