LLM+Graph@VLDB'2025 Workshop Summary

Yixiang Fang; Arijit Khan; Tianxing Wu; Da Yan; Shu Wang

cs.DBApr 3, 2026

LLM+Graph@VLDB'2025 Workshop Summary

Authors: Yixiang Fang, Arijit Khan, Tianxing Wu, Da Yan, Shu Wang

Organizations: The Chinese University of Hong Kong, Shenzhen · Bowling Green State University · Southeast University · Indiana University Bloomington

Abstract

The integration of large language models (LLMs) with graph-structured data has become a pivotal and fast evolving research frontier, drawing strong interest from both academia and industry. The 2nd LLM+Graph Workshop, co-located with the 51st International Conference on Very Large Data Bases (VLDB 2025) in London, focused on advancing algorithms and systems that bridge LLMs, graph data management, and graph machine learning for practical applications. This report highlights the key research directions, challenges, and innovative solutions presented by the workshop's speakers.

LLMs for Graph Learning

Large language models for graph learning combine language understanding with graph structure for prediction, reasoning, and knowledge discovery.

See all 73 papers

LLMs+Graphs: Toward Graph-Native, Synergistic AI Systems
Jun 10, 2026Arijit Khan, Longxu Sun, Xin HuangKnowledge Augmentation for Language Models LLM Reasoning with Graphs
Bowling Green State University, Ohio, USA · Hong Kong Baptist University, Hong Kong, China
Position: How can Graphs Help Large Language Models?
May 4, 2026Xiyuan Wang, Yi Hu, Yanbo Wang +2LLM Grounding Knowledge Augmentation for Language Models
Institute for Artificial Intelligence, Peking University, Beijing 100871, China · School of Computer Sciences, Beijing University of Posts and Telecommunications, Beijing 100876, China
Are Large Language Models Suitable for Graph Computation? Progress and Prospects
Jun 5, 2026Yuting Zhang, Yi Han, Kai Wang +3LLM Reasoning with Graphs LLM Tool Use
University of New South Wales · Antai College of Economics and Management, Shanghai Jiao Tong University · Edith Cowan University +1

Jun 10, 2026J/K move · Enter open · S save

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

Arijit Khan, Longxu Sun, Xin Huang

Bowling Green State University, Ohio, USA · Hong Kong Baptist University, Hong Kong, China

Knowledge Augmentation for Language Models LLM Reasoning with Graphs Graph Retrieval-Augmented Generation LLM Reasoning

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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.

Abstract

LLMs for Graph Learning

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