Graph Structure Learning (GSL) focuses on capturing intrinsic dependencies and interactions among nodes in graph-structured data by generating novel graph structures. Graph Neural Networks (GNNs) have emerged as promising GSL solutions, utilizing recursive message passing to encode node-wise inter-dependencies. However, many existing GSL methods heavily depend on explicit graph structural information as supervision signals, leaving them susceptible to challenges such as data noise and sparsity. In this work, we propose GraphEdit, an approach that leverages large language models (LLMs) to learn complex node relationships in graph-structured data. By enhancing the reasoning capabilities of LLMs through instruction-tuning over graph structures, we aim to overcome the limitations associated with explicit graph structural information and enhance the reliability of graph structure learning. Our approach not only effectively denoises noisy connections but also identifies node-wise dependencies from a global perspective, providing a comprehensive understanding of the graph structure. We conduct extensive experiments on multiple benchmark datasets to demonstrate the effectiveness and robustness of GraphEdit across various settings. We have made our model implementation available at: https://github.com/HKUDS/GraphEdit.

翻译：图结构学习（GSL）致力于通过生成新型图结构，捕捉图结构数据中节点间的内在依赖关系与交互作用。图神经网络（GNN）作为极具前景的GSL解决方案，利用递归消息传递机制编码节点间的相互依赖关系。然而，现有众多GSL方法过度依赖于显式图结构信息作为监督信号，这使其易受数据噪声与稀疏性等挑战的影响。本文提出GraphEdit方法，通过利用大语言模型（LLMs）学习图结构数据中复杂的节点关系。我们采用指令微调技术增强LLMs对图结构的推理能力，旨在克服显式图结构信息带来的局限，提升图结构学习的可靠性。该方法不仅能有效去噪噪声连接，还能从全局视角识别节点间依赖关系，从而提供对图结构的全面理解。我们在多个基准数据集上开展广泛实验，充分验证了GraphEdit在不同场景下的有效性与鲁棒性。模型实现已在https://github.com/HKUDS/GraphEdit公开。