OOD-GraphLLM is a graphLLM framework that jointly optimizes molecular graph representations and biomedical semantic language representations for out-of-distribution drug synergy prediction.
Learning on large-scale text-attributed graphs via variational inference.arXiv preprint arXiv:2210.14709, 2022
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GraphScout trains LLMs to autonomously synthesize structured training data from knowledge graphs via flexible exploration tools, enabling a 4B model to outperform larger LLMs by 16.7% on average with fewer inference tokens and strong cross-domain transfer.
FedLAB organizes multimodal graph knowledge into typed hierarchical codebooks for modality evidence, node semantics, and topology context via federated semantic barycenter pre-training, improving performance by up to 7.53% on benchmarks while enabling semantic traceability.
Bidirectional LLM-GNN co-teaching with round-based pseudo-label preference optimization outperforms golden-teacher baselines on few-shot TAG benchmarks by 3-8% absolute gains.
CANE estimates cluster-specific reliability of noisy LLM pseudo-labels on graphs without ground truth to improve label-free node classification.
L2IR uses LLMs to extract latent intents from behaviors and connections, improving graph fraud detection under camouflage via adaptive self-training and serving as a plug-in for GNN detectors with up to 8.27% AUPRC gain.
DuConTE is a dual-granularity text encoder that incorporates graph topology into language model attention for improved node representations in text-attributed graphs.
A structured survey organizing graph-LLM integration methods by purpose, modality, and strategy across application domains.
GEMS formulates close-ended human-behavior simulation as link prediction on a heterogeneous graph and matches or exceeds LLM performance with three orders of magnitude fewer parameters across three datasets and three evaluation settings.
UltraTAG organizes LLM-GNN methods for text-attributed graphs; UltraTAG-S adds LLM text propagation, augmentation, PageRank node selection, and edge reconfiguration to improve robustness on sparse data, with reported gains of 2.12% and 17.47%.
citing papers explorer
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OOD-GraphLLM: Graph Large Language Model for Out-of-Distribution Generalized Drug Synergy Prediction
OOD-GraphLLM is a graphLLM framework that jointly optimizes molecular graph representations and biomedical semantic language representations for out-of-distribution drug synergy prediction.
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GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning
GraphScout trains LLMs to autonomously synthesize structured training data from knowledge graphs via flexible exploration tools, enabling a 4B model to outperform larger LLMs by 16.7% on average with fewer inference tokens and strong cross-domain transfer.
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FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning
FedLAB organizes multimodal graph knowledge into typed hierarchical codebooks for modality evidence, node semantics, and topology context via federated semantic barycenter pre-training, improving performance by up to 7.53% on benchmarks while enabling semantic traceability.
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Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching
Bidirectional LLM-GNN co-teaching with round-based pseudo-label preference optimization outperforms golden-teacher baselines on few-shot TAG benchmarks by 3-8% absolute gains.
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Where LLM Annotators Fail: Label-Free Learning on Graphs with LLMs
CANE estimates cluster-specific reliability of noisy LLM pseudo-labels on graphs without ground truth to improve label-free node classification.
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L2IR: Revealing Latent Intent in Graph Fraud Detection
L2IR uses LLMs to extract latent intents from behaviors and connections, improving graph fraud detection under camouflage via adaptive self-training and serving as a plug-in for GNN detectors with up to 8.27% AUPRC gain.
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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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Integrating Graphs, Large Language Models, and Agents: Reasoning and Retrieval
A structured survey organizing graph-LLM integration methods by purpose, modality, and strategy across application domains.
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Graph-Based Alternatives to LLMs for Human Simulation
GEMS formulates close-ended human-behavior simulation as link prediction on a heterogeneous graph and matches or exceeds LLM performance with three orders of magnitude fewer parameters across three datasets and three evaluation settings.
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Toward General and Robust LLM-enhanced Text-attributed Graph Learning
UltraTAG organizes LLM-GNN methods for text-attributed graphs; UltraTAG-S adds LLM text propagation, augmentation, PageRank node selection, and edge reconfiguration to improve robustness on sparse data, with reported gains of 2.12% and 17.47%.