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[KDD'2024] "LLM4Graph: A Survey of Large Language Models for Graphs"
| Date | Stars |
|---|---|
| 2026-07-31 | 369 |
| 2026-08-04 | 369 |
| 2026-08-06 | 369 |
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# Awesome-LLM4Graph-Papers
[](https://arxiv.org/abs/2405.08011)
[](https://github.com/HKUDS/Awesome-LLM4Graph-Papers)
<img src="https://badges.pufler.dev/visits/hkuds/Awesome-LLM4Graph-Papers?style=flat-square&logo=github">
A collection of papers and resources about Large Language Models (**LLM**) for Graph Learning (**Graph**).
Graphs are an essential data structure utilized to represent relationships in real-world scenarios. Prior research has established that Graph Neural Networks (GNNs) deliver impressive outcomes in graph-centric tasks, such as link prediction and node classification. Despite these advancements, challenges like data sparsity and limited generalization capabilities continue to persist. Recently, Large Language Models (LLMs) have gained attention in natural language processing. They excel in language comprehension and summarization. Integrating LLMs with graph learning techniques has attracted interest as a way to enhance performance in graph learning tasks.
<p align="center">
<img src="fig/taxonomy.png" alt="Framework" />
</p>
## News
🤗 We're actively working on this project, and your interest is greatly appreciated! To keep up with the latest developments, please consider hit the **STAR** and **WATCH** for updates.
* 🚀 Our [LLM4Graph Survey](https://arxiv.org/abs/2405.08011) is accepted by KDD 2024, and we will also give a lecture-style tutorial there!
* 🔥 We gave a [tutorial](https://llm4graph-tutorial.github.io/) on LLM4Graph at TheWebConf (WWW) 2024!
* Our survey paper: [A Survey of Large Language Models for Graphs](https://arxiv.org/abs/2405.08011) is now ready.
## Overview
This repository serves as a collection of recent advancements in employing large language models (LLMs) for modeling graph-structured data. We categorize and summarize the approaches based on four primary paradigms and nine secondary-level categories. The four primary categories include: 1) GNNs as Prefix, 2) LLMs as Prefix, 3) LLMs-Graphs Intergration, and 4) LLMs-Only
- GNNs as Prefix
<p align='center'>
<img src="fig/GNN_as_prefix.png" width=60% alt="GNNs as Prefix" />
</p>
- LLMs as Prefix
<p align='center'>
<img src="fig/LLM_as_prefix.png" width=60% alt="LLMs as Prefix" />
</p>
- LLMs-Graphs Intergration
<p align='center'>
<img src="fig/LLM-Graph_Intergration.png" width=60% alt="LLMs as Prefix" />
</p>
- LLMs-Only
<p align='center'>
<img src="fig/LLM_only.png" width=60% alt="LLMs as Prefix" />
</p>
We hope this repository proves valuable to your research or practice in the field of self-supervised learning for recommendation systems. If you find it helpful, please consider citing our work:
```bibtex
@inproceedings{ren2024survey,
title={A survey of large language models for graphs},
author={Ren, Xubin and Tang, Jiabin and Yin, Dawei and Chawla, Nitesh and Huang, Chao},
booktitle={Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages={6616--6626},
year={2024}
}
@inproceedings{huang2024large,
title={Large Language Models for Graphs: Progresses and Directions},
author={Huang, Chao and Ren, Xubin and Tang, Jiabin and Yin, Dawei and Chawla, Nitesh},
booktitle={Companion Proceedings of the ACM on Web Conference 2024},
pages={1284--1287},
year={2024}
}
```
## Table of Contents
- [Awesome-LLM4Graph-Papers](#awesome-llm4graph-papers)
- [News](#news)
- [Overview](#overview)
- [Table of Contents](#table-of-contents)
- [Related Resources](#related-resources)
- [🌐 GNNs as Prefix](#-gnns-as-prefix)
- [Node-level Tokenization](#node-level-tokenizatiExcerpt of 14,325 characters
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