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GraphInsight: Unlocking Insights in Large Language Models for Graph Structure Understanding

Yukun Cao, Shuo Han, Zengyi Gao, Zezhong Ding, Xike Xie, S. Kevin Zhou

TL;DR

GraphInsight addresses the challenge of large language models understanding graph structures from language-described graphs, which is hindered by positional biases in long sequences. It introduces two complementary strategies: an importance-based macro-level description reorganization that aligns critical graph elements with strong memory regions, and a lightweight GraphRAG-based external knowledge base for weak-memory regions, enabling retrieval-augmented micro-level understanding. The framework is integrated into LLM agent processes to handle composite graph tasks, and GraphSQA-based experiments show substantial improvements over prompting, reordering, and structural methods across diverse graph sizes and models. These findings highlight a practical path to robust graph-structure comprehension in LLMs, with implications for knowledge graphs, social networks, and other graph-centric domains.

Abstract

Although Large Language Models (LLMs) have demonstrated potential in processing graphs, they struggle with comprehending graphical structure information through prompts of graph description sequences, especially as the graph size increases. We attribute this challenge to the uneven memory performance of LLMs across different positions in graph description sequences, known as ''positional biases''. To address this, we propose GraphInsight, a novel framework aimed at improving LLMs' comprehension of both macro- and micro-level graphical information. GraphInsight is grounded in two key strategies: 1) placing critical graphical information in positions where LLMs exhibit stronger memory performance, and 2) investigating a lightweight external knowledge base for regions with weaker memory performance, inspired by retrieval-augmented generation (RAG). Moreover, GraphInsight explores integrating these two strategies into LLM agent processes for composite graph tasks that require multi-step reasoning. Extensive empirical studies on benchmarks with a wide range of evaluation tasks show that GraphInsight significantly outperforms all other graph description methods (e.g., prompting techniques and reordering strategies) in understanding graph structures of varying sizes.

GraphInsight: Unlocking Insights in Large Language Models for Graph Structure Understanding

TL;DR

GraphInsight addresses the challenge of large language models understanding graph structures from language-described graphs, which is hindered by positional biases in long sequences. It introduces two complementary strategies: an importance-based macro-level description reorganization that aligns critical graph elements with strong memory regions, and a lightweight GraphRAG-based external knowledge base for weak-memory regions, enabling retrieval-augmented micro-level understanding. The framework is integrated into LLM agent processes to handle composite graph tasks, and GraphSQA-based experiments show substantial improvements over prompting, reordering, and structural methods across diverse graph sizes and models. These findings highlight a practical path to robust graph-structure comprehension in LLMs, with implications for knowledge graphs, social networks, and other graph-centric domains.

Abstract

Although Large Language Models (LLMs) have demonstrated potential in processing graphs, they struggle with comprehending graphical structure information through prompts of graph description sequences, especially as the graph size increases. We attribute this challenge to the uneven memory performance of LLMs across different positions in graph description sequences, known as ''positional biases''. To address this, we propose GraphInsight, a novel framework aimed at improving LLMs' comprehension of both macro- and micro-level graphical information. GraphInsight is grounded in two key strategies: 1) placing critical graphical information in positions where LLMs exhibit stronger memory performance, and 2) investigating a lightweight external knowledge base for regions with weaker memory performance, inspired by retrieval-augmented generation (RAG). Moreover, GraphInsight explores integrating these two strategies into LLM agent processes for composite graph tasks that require multi-step reasoning. Extensive empirical studies on benchmarks with a wide range of evaluation tasks show that GraphInsight significantly outperforms all other graph description methods (e.g., prompting techniques and reordering strategies) in understanding graph structures of varying sizes.
Paper Structure (42 sections, 13 equations, 12 figures, 6 tables, 1 algorithm)

This paper contains 42 sections, 13 equations, 12 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. LLMs' Understanding of Graph Structures Through Description Sequences
  4. Positional Bias in Long Sequences for LLMs
  5. Preliminaries
  6. Methodology
  7. Overview
  8. Importance-based Macro-level Graph Understanding
  9. RAG-based Micro-level Graph Understanding
  10. GraphRAG Base.
  11. GraphRAG Process.
  12. Composite Tasks.
  13. Evaluation
  14. Experimental Setup
  15. Baselines.
  16. ...and 27 more sections

Figures (12)

  • Figure 1: Capabilities of LLMs on Graph Structure Understanding
  • Figure 2: Analysis on Positional bias of LLMs
  • Figure 3: Framework of GraphInsight.
  • Figure 4: Analysis on Graphs with Different $|V|$
  • Figure 5: Analysis on Composite Tasks
  • ...and 7 more figures

Theorems & Definitions (3)

  • Definition 1: Sequential Format Graph Description
  • Definition 2: LLMs' Capacity for Graph Understanding
  • Definition 3: Graph Understanding Tasks