Are Large Language Models Suitable for Graph Computation? Progress and Prospects

2026-06-04

AI Summary

This study systematically investigates the applicability and capability boundaries of large language models (LLMs) in graph computation tasks. Addressing structured relational reasoning and algorithmic operations, it proposes the first role-based classification framework that categorizes LLM usage paradigms into “executor” and “planner.” Through a comprehensive literature review and task-specific analysis, the work evaluates LLM performance across diverse graph-related scenarios. The findings indicate that while LLMs demonstrate feasibility on small-scale graph tasks, they remain unreliable for large-scale, precise computations. The paper clarifies the appropriate role of LLMs in graph computing, summarizes existing benchmark datasets, and outlines four key directions for future research to advance this emerging intersection of language models and graph algorithms.

Abstract

Large language models (LLMs) have been increasingly explored for graph computation, where tasks require reasoning over structured relationships and algorithmic operations. Yet, it remains unclear when LLMs can reliably support such computation and how they should be incorporated into graph-solving pipelines. Existing surveys at the intersection of LLMs and graphs primarily focus on graph learning, text-attributed graphs, or graph-language modeling. To bridge this gap, we provide a comprehensive review of LLMs for graph computation through a role-based taxonomy. Specifically, we identify two major paradigms: i) LLMs as executors, where models directly solve graph tasks from graph descriptions and instructions; and ii) LLMs as planners, where models formulate problems, decompose reasoning steps, and invoke external tools or agents for execution. Based on this taxonomy, we analyze the strengths and limitations of current methods. Our review indicates that LLMs are promising for simple, small-scale tasks, but remain unreliable for large-scale and exactness-demanding tasks. Finally, we summarize available datasets and suggest four future directions.

Problem

• Large Language Models
• Graph Computation
• Reliability
• Integration

Innovation

• role-based taxonomy
• LLMs as executors
• LLMs as planners
• graph computation
• large language models