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A curated list of papers and resources based on "Large Language Models on Graphs: A Comprehensive Survey" (TKDE)
| Date | Stars |
|---|---|
| 2026-07-31 | 996 |
| 2026-08-04 | 997 |
| 2026-08-06 | 997 |
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# Awesome-Language-Model-on-Graphs [](https://awesome.re)
A curated list of papers and resources about large language models (LLMs) on graphs based on our survey paper: [Large Language Models on Graphs: A Comprehensive Survey](https://arxiv.org/abs/2312.02783).
**An awesome repo on multimodal learning on graphs an be found [here](https://github.com/PeterGriffinJin/Awesome-Multimodal-on-Graphs)**.
**This repo will be continuously updated. Don't forget to star <img src="./fig/star.svg" width="15" height="15" /> it and keep tuned!**
**Please cite the paper in [Citations](#citations) if you find the resource helpful for your research. Thanks!**
<p align="center">
<img src="./fig/intro.svg" width="90%" style="align:center;"/>
</p>
## Why LLMs on graphs?
Large language models (LLMs), such as ChatGPT and LLaMA, are creating significant advancements in natural language processing, due to their strong text encoding/decoding ability and newly found emergent capability (e.g., reasoning).
While LLMs are mainly designed to process pure texts, there are many real-world scenarios where text data are associated with rich structure information in the form of graphs (e.g., academic networks, and e-commerce networks) or scenarios where graph data are captioned with rich textual information (e.g., molecules with descriptions).
Besides, although LLMs have shown their pure text-based reasoning ability, it is underexplored whether such ability can be generalized to graph scenarios (i.e., graph-based reasoning).
In this paper, we provide a comprehensive review of scenarios and techniques related to large language models on graphs.
## Contents
- [Awesome-Language-Model-on-Graphs ](#awesome-language-model-on-graphs-)
- [Why LLMs on graphs?](#why-llms-on-graphs)
- [Contents](#contents)
- [Keywords Convention](#keywords-convention)
- [Perspectives](#perspectives)
- [Pure Graphs](#pure-graphs)
- [Datasets](#datasets)
- [Direct Answering](#-direct-answering)
- [Heuristic Reasoning](#-heuristic-reasoning)
- [Algorithmic Reasoning](#-algorithmic-reasoning)
- [Text-Attributed Graphs](#text-attributed-graphs)
- [Datasets](#datasets-1)
- [LLM as Predictor (Node)](#-llm-as-predictor-node)
- [Graph As Sequence (Node)](#graph-as-sequence-node)
- [Graph-Empowered LLM (Node)](#graph-empowered-llm-node)
- [Graph-Aware LLM Finetuning](#graph-aware-llm-finetuning)
- [LLM as Encoder](#-llm-as-encoder)
- [Optimization](#optimization)
- [Data Augmentation](#data-augmentation)
<!-- - [Knowledge Distillation](#knowledge-distillation) -->
- [Efficiency](#efficiency)
- [LLM as Aligner (Node)](#-llm-as-aligner-node)
- [Prediction Alignment](#prediction-alignment)
- [Latent Space Alignment (Node)](#latent-space-alignment-node)
- [Text-Paired Graphs (Molecules)](#text-paired-graphs-molecules)
- [Datasets](#datasets-2)
- [LLM as Predictor (Graph)](#-llm-as-predictor-graph)
- [Graph As Sequence](#graph-as-sequence)
- [Graph-Empowered LLM (Graph)](#graph-empowered-llm-graph)
- [LLM as Aligner (Graph)](#-llm-as-aligner-graph)
- [Latent Space Alignment (Graph)](#latent-space-alignment-graph)
- [Others](#others)
- [Contribution](#contribution)
- [Citations](#citations)
### Keywords Convention
 The Transformer architecture used in the work, e.g., EncoderOnly, DecoderOnly, EncoderDecoder.
 The size of the large language model, e.g., medium (i.e., less than 1B parameters), LLM (i.e., more than 1B parameters).
## Perspectives
1. **Unifying Large Language Models and Knowledge Graphs: A Roadmap.** `preprint`
*Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, Xindong Wu* [[PDF](https://arxiv.org/pdf/2306.08302.pdf)], 2023.6
2. **Integrating Graphs with Large Language Models: Methods and ProExcerpt of 62,598 characters
Read on GitHub
Bowen Jin · OpenAI · United States
125
4
4
2
2
Xixi Wu · The Chinese University of Hong Kong · Hong Kong
1
1
Zihao Li · University of Illinois Urbaba-Champaign · United States
1
Shengyuan Chen
1
Erwan Le Merrer · Inria
1
karthik-soman · SAP · United States
1
1
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