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.
InProceedings of the AAAI Conference on Artificial Intelligence
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A structured survey organizing graph-LLM integration methods by purpose, modality, and strategy across application domains.
A GNN-encoded subgraph soft prompting method lets LLMs perform topology-aware reasoning over incomplete KGs for KBQA, reaching SOTA on three of four benchmarks via a two-stage LLM pipeline.
A framework for inference-time knowledge graph construction and expansion improves factual accuracy in LLMs on three QA benchmarks by combining internal LLM knowledge with selective external retrieval.
citing papers explorer
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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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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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Topology-Aware Reasoning over Incomplete Knowledge Graph with Graph-Based Soft Prompting
A GNN-encoded subgraph soft prompting method lets LLMs perform topology-aware reasoning over incomplete KGs for KBQA, reaching SOTA on three of four benchmarks via a two-stage LLM pipeline.
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Improving Factuality in LLMs via Inference-Time Knowledge Graph Construction
A framework for inference-time knowledge graph construction and expansion improves factual accuracy in LLMs on three QA benchmarks by combining internal LLM knowledge with selective external retrieval.