mlsanityData to 14 Oct 2026

LPNL: Scalable Link Prediction with Large Language Models

arXiv 2401.13227 ↗First posted 24 Jan 2024cs.CL cs.AI cs.LGcited by 7

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Abstract (arXiv)

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 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.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 31 of its 49 references that are in the map. First posted on arXiv 24 Jan 2024.

Builds on

31 of 49 references.

References · 31

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19 more
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Cited by year

Citing papers per year: 2024: 4; 2025: 3.

7 papers.

  1. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Modelscited by 88
  2. 2025Parameters vs. Context: Fine-Grained Control of Knowledge Reliance in Language Modelscited by 39
  3. 2024A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applicationscited by 17
  4. 2024KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledgecited by 16
  5. 2024HiddenGuard: Fine-Grained Safe Generation with Specialized Representation Routercited by 16
  6. 2024GUNDAM: Aligning Large Language Models with Graph Understandingcited by 5
  7. 2025An Interdisciplinary and Cross-Task Review on Missing Data Imputationcited by 0