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| Xubin Ren1, | Jiabin Tang1, | Lianghao Xia1, | Dawei Yin2, | Nitesh Chawla3 | Chao Huang1, |
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.
We will explore four primary categories of methods that harness the power of large language models (LLMs) in graph tasks. These are: i) Graph Neural Networks (GNNs) as Prefix, ii) LLMs as Prefix, iii) LLMs-Graphs Integration, and iv) LLMs-Only. In each section, we will introduce you to the leading techniques in this field and provide you with sample code snippets to experiment with. This tutorial is designed to be valuable for both researchers aiming to pioneer new LLM4Graph solutions and industry professionals seeking to apply these methods in practical, real-world scenarios.
| Time | Section | Presenter |
|---|---|---|
| 14:00 - 14:10 | Opening & Introduction | Prof. Chao Huang |
| 14:10 - 14:40 | Section 1: GNNs as Prefix | Jiabin Tang |
| 14:40 - 14:45 | Q & A for Section 1 | Jiabin Tang |
| 14:45 - 15:15 | Section 2: LLMs as Prefix | Lianghao Xia |
| 15:15 - 15:20 | Q & A for Section 2 | Lianghao Xia |
| 15:20 - 15:50 | Section 3: LLMs-Graphs Intergration | Xubin Ren |
| 15:50 - 15:55 | Q & A for Section 3 | Xubin Ren |
| 15:55 - 16:25 | Coffee Break | - |
| 16:25 - 16:55 | Section 4: LLMs-Only | Jiabin Tang & Xubin Ren |
| 16:55 - 17:00 | Q & A for Section 4 | Jiabin Tang & Xubin Ren |
Works in our group are highlighted in bold.
@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}
}