Recent research has explored the use of Large Language Models (LLMs) for tackling complex graph reasoning tasks. However, due to the intricacies of graph structures and the inherent limitations of LLMs in handling long text, current approaches often fail to deliver satisfactory accuracy, even on small-scale graphs and simple tasks. To address these challenges, we introduce GraphAgent-Reasoner, a fine-tuning-free framework that utilizes a multi-agent collaboration strategy for explicit and precise graph reasoning. Inspired by distributed graph computation theory, our framework decomposes graph problems into smaller, node-centric tasks that are distributed among multiple agents. The agents collaborate to solve the overall problem, significantly reducing the amount of information and complexity handled by a single LLM, thus enhancing the accuracy of graph reasoning. By simply increasing the number of agents, GraphAgent-Reasoner can efficiently scale to accommodate larger graphs with over 1,000 nodes. Evaluated on the GraphInstruct dataset, our framework demonstrates near-perfect accuracy on polynomial-time graph reasoning tasks, significantly outperforming the best available models, both closed-source and fine-tuned open-source variants. Our framework also demonstrates the capability to handle real-world graph reasoning applications such as webpage importance analysis.

翻译：近期研究探索了利用大语言模型（LLMs）处理复杂图推理任务的可能性。然而，由于图结构的复杂性以及大语言模型在处理长文本方面的固有局限，现有方法即使在小型图和简单任务上也往往难以达到令人满意的准确率。为应对这些挑战，我们提出了GraphAgent-Reasoner——一个无需微调的框架，其采用多智能体协作策略实现显式且精确的图推理。受分布式图计算理论启发，本框架将图问题分解为更小的、以节点为中心的子任务，并将其分配给多个智能体。智能体通过协作解决整体问题，显著减少了单个大语言模型需处理的信息量和复杂度，从而提升了图推理的准确性。通过简单地增加智能体数量，GraphAgent-Reasoner能够高效扩展以适应超过1000个节点的大规模图。在GraphInstruct数据集上的评估表明，本框架在多项式时间图推理任务上实现了接近完美的准确率，显著优于当前最佳的闭源模型及经过微调的开源变体。我们的框架还展示了处理实际图推理应用（如网页重要性分析）的能力。