Large Language Models Meet Text-Attributed Graphs: A Survey of Integration Frameworks and Applications

TL;DR

This survey reviews how large language models and text-attributed graphs can be integrated to enhance reasoning, interpretability, and application performance, highlighting strategies, datasets, and future challenges.

Contribution

It introduces a novel taxonomy for LLM-TAG integration, categorizes orchestration strategies, and summarizes empirical insights and applications in this emerging field.

Findings

LLMs and TAGs provide complementary benefits when combined.

Various orchestration strategies improve task performance.

Integration enhances reasoning and interpretability in applications.

Abstract

Large Language Models (LLMs) have achieved remarkable success in natural language processing through strong semantic understanding and generation. However, their black-box nature limits structured and multi-hop reasoning. In contrast, Text-Attributed Graphs (TAGs) provide explicit relational structures enriched with textual context, yet often lack semantic depth. Recent research shows that combining LLMs and TAGs yields complementary benefits: enhancing TAG representation learning and improving the reasoning and interpretability of LLMs. This survey provides the first systematic review of LLM--TAG integration from an orchestration perspective. We introduce a novel taxonomy covering two fundamental directions: LLM for TAG, where LLMs enrich graph-based tasks, and TAG for LLM, where structured graphs improve LLM reasoning. We categorize orchestration strategies into sequential, parallel, and multi-module frameworks, and discuss advances in TAG-specific pretraining, prompting, and parameter-efficient fine-tuning. Beyond methodology, we summarize empirical insights, curate available datasets, and highlight diverse applications across recommendation systems, biomedical analysis, and knowledge-intensive question answering. Finally, we outline open challenges and promising research directions, aiming to guide future work at the intersection of language and graph learning.