🤖 AI Summary
This work addresses the limitations of large language models (LLMs) in structured data processing and multi-hop reasoning by proposing a graph-native collaborative intelligence framework. The approach integrates three key innovations: graph-augmented retrieval-based reasoning, bidirectional synergy between LLMs and knowledge graphs, and graph algorithm–driven agent decision-making. By deeply unifying LLMs, graph neural networks (GNNs), and graph computing techniques, the framework cohesively combines natural language interfaces, hybrid LLM-GNN pipelines, and graph data management systems. This integration substantially enhances fact consistency and context-aware reasoning and decision-making capabilities in complex scenarios, laying the foundation for next-generation graph-native intelligent systems.
📝 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.