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Exploring the application of large-scale language models to graph learning isa novel endeavor. However, the vast amount of information inherent in largegraphs poses significant challenges to this process. This paper focuses on thelink prediction task and introduces LPNL (Link Prediction via NaturalLanguage), a framework based on a large language model designed for scalablelink prediction on large-scale heterogeneous graphs.We design novel prompts forlink prediction that articulate graph details in natural language. We propose atwo-stage sampling pipeline to extract crucial information from large-scaleheterogeneous graphs, and a divide-and-conquer strategy to control the inputtoken count within predefined limits, addressing the challenge of overwhelminginformation. We fine-tune a T5 model based on our self-supervised learningdesigned for for link prediction. Extensive experiments on a large publicheterogeneous graphs demonstrate that LPNL outperforms various advancedbaselines, highlighting its remarkable performance in link prediction tasks onlarge-scale graphs.

Scalable Link Prediction on Large-Scale Heterogeneous Graphs with Large Language Models