LLMs+Graphs: Toward Graph-Native, Synergistic AI Systems

Arijit Khan; Longxu Sun; Xin Huang

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arXiv · 2606.11560

LLMs+Graphs: Toward Graph-Native, Synergistic AI Systems

Arijit Khan·Longxu Sun·Xin Huang

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Abstract

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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BibTeX RIS

Arijit Khan, Longxu Sun, Xin Huang. 2026-06-10. LLMs+Graphs: Toward Graph-Native, Synergistic AI Systems. https://arxiv.org/abs/2606.11560

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