LPNL: Scalable Link Prediction with Large Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910336363266048 |
|---|---|
| author | Bi, Baolong Liu, Shenghua Wang, Yiwei Mei, Lingrui Cheng, Xueqi |
| author_facet | Bi, Baolong Liu, Shenghua Wang, Yiwei Mei, Lingrui Cheng, Xueqi |
| contents | Exploring the application of large language models (LLMs) to graph learning is a emerging endeavor. However, the vast amount of information inherent in large graphs poses significant challenges to this process. This work focuses on the link prediction task and introduces $\textbf{LPNL}$ (Link Prediction via Natural Language), a framework based on large language models designed for scalable link prediction on large-scale heterogeneous graphs. We design novel prompts for link prediction that articulate graph details in natural language. We propose a two-stage sampling pipeline to extract crucial information from the graphs, and a divide-and-conquer strategy to control the input tokens within predefined limits, addressing the challenge of overwhelming information. We fine-tune a T5 model based on our self-supervised learning designed for link prediction. Extensive experimental results demonstrate that LPNL outperforms multiple advanced baselines in link prediction tasks on large-scale graphs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_13227 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LPNL: Scalable Link Prediction with Large Language Models Bi, Baolong Liu, Shenghua Wang, Yiwei Mei, Lingrui Cheng, Xueqi Computation and Language Artificial Intelligence Machine Learning Social and Information Networks Exploring the application of large language models (LLMs) to graph learning is a emerging endeavor. However, the vast amount of information inherent in large graphs poses significant challenges to this process. This work focuses on the link prediction task and introduces $\textbf{LPNL}$ (Link Prediction via Natural Language), a framework based on large language models designed for scalable link prediction on large-scale heterogeneous graphs. We design novel prompts for link prediction that articulate graph details in natural language. We propose a two-stage sampling pipeline to extract crucial information from the graphs, and a divide-and-conquer strategy to control the input tokens within predefined limits, addressing the challenge of overwhelming information. We fine-tune a T5 model based on our self-supervised learning designed for link prediction. Extensive experimental results demonstrate that LPNL outperforms multiple advanced baselines in link prediction tasks on large-scale graphs. |
| title | LPNL: Scalable Link Prediction with Large Language Models |
| topic | Computation and Language Artificial Intelligence Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2401.13227 |