title: A Graph Perspective to Probe Structural Patterns of Knowledge in Large
    Language Models
summary: Large language models have been extensively studied as neural knowledge bases
  for their knowledge access, editability, reasoning, and explainability.
  However, few works focus on the structural patterns of their knowledge.
  Motivated by this gap, we investigate these structural patterns from a graph
  perspective. We quantify the knowledge of LLMs at both the triplet and entity
  levels, and analyze how it relates to graph structural properties such as node
  degree. Furthermore, we uncover the knowledge homophily, where topologically
  close entities exhibit similar levels of knowledgeability, which further
  motivates us to develop graph machine learning models to estimate entity
  knowledge based on its local neighbors. This model further enables valuable
  knowledge checking by selecting triplets less known to LLMs. Empirical results
  show that using selected triplets for fine-tuning leads to superior
  performance.
paper_authors._id: 6839220cf85de1fc56402b3d
paper_authors.name: Utkarsh Sahu
paper_authors._id: 6839220cf85de1fc56402b3e
paper_authors.name: Zhisheng Qi
paper_authors._id: 6839220cf85de1fc56402b3f
paper_authors.name: Yongjia Lei
paper_authors._id: 6839220cf85de1fc56402b40
paper_authors.name: Ryan A. Rossi
paper_authors._id: 6839220cf85de1fc56402b41
paper_authors.name: Franck Dernoncourt
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-30T06:53:31.804Z
paper_authors._id: 62c5947524171688a9feb992
paper_authors.avatarUrl: /avatars/5a151713b9eae8dc566f5957acee3475.svg
paper_authors.fullname: Franck Dernoncourt
paper_authors.type: user
paper_authors.user: Franck-Dernoncourt
paper_authors._id: 6839220cf85de1fc56402b42
paper_authors.name: Nesreen K. Ahmed
paper_authors._id: 6839220cf85de1fc56402b43
paper_authors.name: Mahantesh M Halappanavar
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-30T05:25:34.615Z
paper_authors._id: 637c6d95a8716d642050b50f
paper_authors.avatarUrl: /avatars/0955a10113807348f24db968c7bd7c7a.svg
paper_authors.fullname: Mahantesh Halappanavar
paper_authors.type: user
paper_authors.user: mhalappa
paper_authors._id: 6839220cf85de1fc56402b44
paper_authors.name: Yao Ma
paper_authors._id: 6839220cf85de1fc56402b45
paper_authors.name: Yu Wang
paper_title: A Graph Perspective to Probe Structural Patterns of Knowledge in Large
    Language Models
paper_summary: Large language models have been extensively studied as neural knowledge bases
  for their knowledge access, editability, reasoning, and explainability.
  However, few works focus on the structural patterns of their knowledge.
  Motivated by this gap, we investigate these structural patterns from a graph
  perspective. We quantify the knowledge of LLMs at both the triplet and entity
  levels, and analyze how it relates to graph structural properties such as node
  degree. Furthermore, we uncover the knowledge homophily, where topologically
  close entities exhibit similar levels of knowledgeability, which further
  motivates us to develop graph machine learning models to estimate entity
  knowledge based on its local neighbors. This model further enables valuable
  knowledge checking by selecting triplets less known to LLMs. Empirical results
  show that using selected triplets for fine-tuning leads to superior
  performance.
paper_ai_summary: The study explores the structural patterns of knowledge in large language models from a graph perspective, uncovering knowledge homophily and developing models for graph machine learning to estimate entity knowledge.
paper_ai_keywords: large language models graph perspective triplet entity node degree knowledge homophily graph machine learning knowledge checking fine-tuning