LLM+Graph@VLDB'2025 Workshop Summary

TL;DR

This workshop summary discusses recent advances and challenges in integrating large language models with graph data, emphasizing new algorithms and systems for practical applications.

Contribution

It provides an overview of the latest research directions, challenges, and solutions in LLM and graph data integration from the VLDB 2025 workshop.

Findings

Identified key research directions in LLM+graph integration.

Highlighted innovative algorithms and systems for practical use.

Outlined challenges and future research opportunities.

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.