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

• URL: http://arxiv.org/abs/2510.21131v1
• Date: Fri, 24 Oct 2025 03:53:00 GMT
• Title: Large Language Models Meet Text-Attributed Graphs: A Survey of Integration Frameworks and Applications
• Authors: Guangxin Su, Hanchen Wang, Jianwei Wang, Wenjie Zhang, Ying Zhang, Jian Pei,
• Abstract summary: Large Language Models (LLMs) have achieved remarkable success in natural language processing through strong semantic understanding and generation.<n>Recent research shows that combining LLMs and Text-Attributed Graphs (TAGs) yields complementary benefits.<n>This survey provides the first systematic review of LLM--TAG integration from an orchestration perspective.
• Score: 14.773541046977355
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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