梳理图与大模型融合的适用场景与方法，助研究者选对技术路径。
Integrating Graphs, Large Language Models, and Agents: Reasoning and Retrieval
- 按任务目标、图类型和融合方式分类整合现有方法。
- 覆盖安全、医疗、金融等多领域，明确各技术优劣与适用条件。
- 为不同任务需求提供可操作的技术选型指南，适合科研与工程参考。
生成式AI，尤其是大语言模型，越来越多地结合基于图的表示以增强推理、检索和结构化决策能力。尽管进展迅速，但关于在何种场景、为何以及何种类型的图-大模型融合最适宜仍缺乏清晰认识。本综述系统梳理了图与大语言模型集成的设计选择，依据任务目的（推理、检索、生成、推荐）、图模态（知识图谱、场景图、交互图、因果图、依赖图）及融合策略（提示、增强、训练或基于代理使用）进行分类。通过映射网络安全、医疗、材料科学、金融、机器人及多模态环境中的代表性工作，揭示各类技术的优势、局限及最佳适配场景。旨在为研究者根据任务需求、数据特征与推理复杂度，选择最合适的图-大模型方法提供实用指导。
原文摘要 · Abstract (English)
Generative AI, particularly Large Language Models, increasingly integrates graph-based representations to enhance reasoning, retrieval, and structured decision-making. Despite rapid advances, there remains limited clarity regarding when, why, where, and what types of graph-LLM integrations are most appropriate across applications. This survey provides a concise, structured overview of the design choices underlying the integration of graphs with LLMs. We categorize existing methods based on their purpose (reasoning, retrieval, generation, recommendation), graph modality (knowledge graphs, scene graphs, interaction graphs, causal graphs, dependency graphs), and integration strategies (prompting, augmentation, training, or agent-based use). By mapping representative works across domains such as cybersecurity, healthcare, materials science, finance, robotics, and multimodal environments, we highlight the strengths, limitations, and best-fit scenarios for each technique. This survey aims to offer researchers a practical guide for selecting the most suitable graph-LLM approach depending on task requirements, data characteristics, and reasoning complexity.
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