让大模型与图计算协同，构建更智能的下一代图原生AI系统。
LLMs+Graphs: Toward Graph-Native, Synergistic AI Systems
- 大模型结合图计算提升检索与多跳推理能力
- 大模型与知识图谱双向赋能，增强语义一致性
- 适合数据科学、图学习与智能代理研究者参考
大型语言模型（LLMs）发展迅速，但在结构化与多跳推理方面存在局限，亟需构建图原生、协同型人工智能系统。图结构数据支撑社交、生物、金融、交通、网络及知识等关键领域应用，理解如何利用图计算实现基于上下文的精准推理至关重要。当前三大协同趋势浮现：一是将图计算融入大模型以增强检索与推理；二是大模型与知识图谱（KGs）双向集成，大模型辅助知识图谱构建与维护，知识图谱则约束语义与事实一致性；三是图算法增强智能体在规划、决策与多步推理中的能力。同时，大模型通过自然语言接口和混合式大模型-图神经网络（GNN）管道，为图数据管理与图机器学习带来新能力。本教程整合驱动这些融合方向的算法、系统与设计原则，为数据科学与数据挖掘研究者提供统一视角，推动将大模型、图数据管理、图挖掘、图机器学习与智能体计算融合至下一代图原生AI系统。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have advanced rapidly, but their limitations in structured and multi-hop reasoning underscore the need for graph-native, synergistic artificial intelligence (AI) systems. Graph-structured data underpins critical applications across social, biological, financial, transportation, web, and knowledge domains, making it essential to understand how LLMs can leverage graph computation for grounded, context-rich inference. Three complementary synergies are emerging: LLMs augmented with graph computation for retrieval and reasoning; bidirectional integration between LLMs and knowledge graphs (KGs), where LLMs support KG construction and curation while KGs enforce semantic constraints and factual consistency; and AI agents strengthened by graph algorithms for planning, decision making, and multi-step reasoning. In parallel, LLMs introduce new capabilities for graph data management and graph machine learning (ML) through natural language interfaces and hybrid LLM-graph neural network (GNN) pipelines. This tutorial synthesizes the algorithms, systems, and design principles driving these converging directions, offering data science and data mining researchers a unified perspective on integrating LLMs, graph data management, graph mining, graph ML, and agentic computation into next-generation graph-native AI systems.
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