Scalable Link Prediction on Large-Scale Heterogeneous Graphs with Large Language Models
CoRR(2024)
摘要
Exploring the application of large-scale language models to graph learning is
a novel endeavor. However, the vast amount of information inherent in large
graphs poses significant challenges to this process. This paper focuses on the
link prediction task and introduces LPNL (Link Prediction via Natural
Language), a framework based on a large language model 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 large-scale
heterogeneous graphs, and a divide-and-conquer strategy to control the input
token count within predefined limits, addressing the challenge of overwhelming
information. We fine-tune a T5 model based on our self-supervised learning
designed for for link prediction. Extensive experiments on a large public
heterogeneous graphs demonstrate that LPNL outperforms various advanced
baselines, highlighting its remarkable performance in link prediction tasks on
large-scale graphs.
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