  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.
