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.

翻译：大型语言模型（LLMs）已取得快速发展，但其在结构化与多跳推理方面的局限性，凸显了对图原生、协同人工智能（AI）系统的需求。图结构数据支撑着社交、生物、金融、交通、网络及知识领域的关键应用，因此理解LLMs如何利用图计算实现基于上下文的扎实推理至关重要。当前正涌现出三种互补的协同模式：LLMs通过图计算增强检索与推理；LLMs与知识图谱（KGs）间的双向集成，其中LLMs支持KG构建与管理，而KG则施加语义约束与事实一致性；以及通过图算法强化规划、决策与多步推理能力的AI智能体。与此同时，LLMs通过自然语言界面和混合LLM-图神经网络（GNN）流水线，为图数据管理与图机器学习（ML）带来了新能力。本教程综合阐述了驱动这些融合方向的算法、系统与设计原则，为数据科学和数据挖掘研究者提供了将LLMs、图数据管理、图挖掘、图ML及智能体计算集成为下一代图原生AI系统的统一视角。