让大模型像人一样探索图数据，动态调用工具解决大规模图分析难题
GraphChain: Large Language Models for Large-scale Graph Analysis via Tool Chaining
- 通过动态工具链模拟人类探索式推理，突破大模型处理图数据的局限
- 在多个真实图数据集上表现优于现有方法，实现可扩展的智能分析
- 适合需要复杂图推理的科研与工业场景，如社交网络、生物网络分析
大语言模型在处理大规模图数据时面临上下文长度限制和推理僵化的问题。本文提出GraphChain框架，通过动态调用一系列专用工具，使大模型能够像人类一样进行探索性分析。该框架引入两项关键创新：（1）渐进式图压缩机制，基于强化学习生成兼顾任务相关性与信息压缩率的最优工具序列；（2）结构感知的测试时自适应策略，利用图的谱特性与轻量适配器高效调整工具选择，无需昂贵重训练。实验表明，GraphChain显著优于现有方法，在多个真实图数据集上实现了可扩展且自适应的大模型驱动图分析。
原文摘要 · Abstract (English)
Large Language Models (LLMs) face significant limitations when applied to large-scale graphs, struggling with context constraints and inflexible reasoning. We present GraphChain, a framework that enables LLMs to analyze complex graphs through dynamic sequences of specialized tools, mimicking human exploratory intelligence. Our approach introduces two key innovations: (1) Progressive Graph Distillation, a reinforcement learning mechanism that generates optimized tool sequences balancing task relevance with information compression, and (2) Structure-aware Test-Time Adaptation, which efficiently tailors tool selection strategies to diverse graph topologies using spectral properties and lightweight adapters without costly retraining. Experiments show GraphChain significantly outperforms prior methods, enabling scalable and adaptive LLM-driven graph analysis.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。