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

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. Among these domains, leveraging LLMs to tackle applications involving graphs has emerged as a burgeoning field of research, as graphs represent fundamental structures that capture intricate relationships and interactions in the real world Wang et al. (2021); Xu (2021). For example, Fatemi et al. have explored the potential of LLMs by converting various types of graphs, such as knowledge graphs Baek et al. (2023); Pan et al. (2024) and social network graphs Santra (2024); Babic (2023), into natural language descriptions, thereby enabling LLMs to perform question-answering tasks related to these graphs. A key observation is that enhancing LLM performance in graph-related applications depends critically on LLMs' ability to comprehend graph structures through natural language descriptions. Existing studies Shang & Huang (2024); Li et al. (2023) primarily utilizes two direct methods to transform graphs into text inputs for LLMs: the structural format transforming, such as adjacency matrices (termed as AM) or lists (termed as AL) and the sequential format transforming, such as edge-by-edge These authors contributed equally to this work. However, extensive empirical studies Yuan et al. (2024) have shown that LLMs face significant challenges in understanding and reasoning about graph structures using current graph transformation methods, especially as graph size increases, leading to a "comprehension collapse". As shown in Figure 1 (a), several common LLMs perform poorly on graph structure understanding tasks (see benchmarks in Section 5.1), and their comprehension declines sharply as the graph size increases, ultimately leading to complete failure.