Integrating Graphs, Large Language Models, and Agents: Reasoning and Retrieval

2026-04-17

AI Summary

This study addresses the lack of systematic understanding regarding the applicability, timing, and methodology of integrating graph structures with large language models (LLMs) across diverse scenarios. It proposes a structured analytical framework that categorizes existing graph-LLM approaches according to task objectives, graph types, and integration strategies—encompassing techniques such as prompt engineering, data augmentation, joint training, and agent-based architectures—and covering multiple graph modalities including knowledge graphs and causal graphs. Through cross-domain evaluation, the work identifies optimal practices and boundary conditions for various fusion schemes in tasks like reasoning and retrieval, offering researchers a principled guideline for selecting appropriate methods based on task requirements, data characteristics, and reasoning complexity.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Generative AI, particularly Large Language Models, increasingly integrates graph-based representations to enhance reasoning, retrieval, and structured decision-making. Despite rapid advances, there remains limited clarity regarding when, why, where, and what types of graph-LLM integrations are most appropriate across applications. This survey provides a concise, structured overview of the design choices underlying the integration of graphs with LLMs. We categorize existing methods based on their purpose (reasoning, retrieval, generation, recommendation), graph modality (knowledge graphs, scene graphs, interaction graphs, causal graphs, dependency graphs), and integration strategies (prompting, augmentation, training, or agent-based use). By mapping representative works across domains such as cybersecurity, healthcare, materials science, finance, robotics, and multimodal environments, we highlight the strengths, limitations, and best-fit scenarios for each technique. This survey aims to offer researchers a practical guide for selecting the most suitable graph-LLM approach depending on task requirements, data characteristics, and reasoning complexity.

Problem

• Graph-LLM integration
• reasoning
• retrieval
• structured decision-making
• generative AI

Innovation

• graph-LLM integration
• reasoning
• retrieval
• agent-based LLMs
• structured knowledge