Graph embedding methods such as Graph Neural Networks (GNNs) and Graph Transformers have contributed to the development of graph reasoning algorithms for various tasks on knowledge graphs. However, the lack of interpretability and explainability of graph embedding methods has limited their applicability in scenarios requiring explicit reasoning. In this paper, we introduce the Graph Agent (GA), an intelligent agent methodology of leveraging large language models (LLMs), inductive-deductive reasoning modules, and long-term memory for knowledge graph reasoning tasks. GA integrates aspects of symbolic reasoning and existing graph embedding methods to provide an innovative approach for complex graph reasoning tasks. By converting graph structures into textual data, GA enables LLMs to process, reason, and provide predictions alongside human-interpretable explanations. The effectiveness of the GA was evaluated on node classification and link prediction tasks. Results showed that GA reached state-of-the-art performance, demonstrating accuracy of 90.65%, 95.48%, and 89.32% on Cora, PubMed, and PrimeKG datasets, respectively. Compared to existing GNN and transformer models, GA offered advantages of explicit reasoning ability, free-of-training, easy adaption to various graph reasoning tasks

翻译：图嵌入方法（如图神经网络和图变换器）推动了知识图谱上各类任务中图推理算法的发展。然而，图嵌入方法缺乏可解释性和可说明性，限制了其在需要显式推理场景中的适用性。本文提出图智能体（Graph Agent, GA）——一种利用大语言模型、归纳-演绎推理模块和长期记忆进行知识图谱推理任务的智能体方法。GA融合了符号推理与现有图嵌入方法的优势，为复杂图推理任务提供了创新方案。通过将图结构转化为文本数据，GA使大语言模型能够处理、推理并生成可解释的预测结果。我们在节点分类和链路预测任务上评估了GA的有效性。结果显示，GA达到了最先进性能，在Cora、PubMed和PrimeKG数据集上分别实现了90.65%、95.48%和89.32%的准确率。与现有图神经网络和Transformer模型相比，GA具有显式推理能力、免训练特性，并能轻松适配各类图推理任务。