Graph4LLM: A Systematic Survey of Graph-Enhanced Large Language Models
Xinyan Zhu, Cheng Yang, Qiuyu Wang, Zeyuan Guo, Yiding Wang, Zedi Liu, Chunchen Wang, Chuan Shi
Abstract
Large Language Models (LLMs) excel in natural language processing (NLP) tasks. However, they suffer from inherent limitations due to their sequence-based nature, such as structural information loss and factual unreliability. Graphs, with the ability to explicitly model entities and relations, offer an effective way to address these shortcomings. To systematically synthesize the emerging research on graph-enhanced LLMs, this survey, Graph4LLM, examines how these methods integrate graphs into various stages of the LLM pipeline, including the input, model, and output phases. For each phase, we provide a detailed review of the key methods and techniques. We also introduce a wide range of application scenarios where Graph4LLM methods demonstrate significant potential. Finally, we outline the challenges and future research directions for developing more efficient and interpretable solutions.
BibTeX
@inproceedings{ijcai2026_graph4llmasystem,
title = {Graph4LLM: A Systematic Survey of Graph-Enhanced Large Language Models},
author = {Xinyan Zhu and Cheng Yang and Qiuyu Wang and Zeyuan Guo and Yiding Wang and Zedi Liu and Chunchen Wang and Chuan Shi},
booktitle = {IJCAI 2026},
year = {2026}
}