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.

翻译：图结构学习（GSL）专注于通过生成新颖的图结构来捕捉图结构数据中节点间的固有依赖关系与交互作用。图神经网络（GNN）利用递归消息传递机制编码节点间相互依赖关系，已成为极具前景的图结构学习解决方案。然而，现有许多图结构学习方法严重依赖显式图结构信息作为监督信号，易受数据噪声和稀疏性等挑战影响。本研究提出GraphEdit方法，利用大型语言模型（LLM）学习图结构数据中的复杂节点关系。通过对图结构进行指令微调以增强LLM的推理能力，我们旨在克服依赖显式图结构信息的局限性，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，提供对图结构的全面理解。我们在多个基准数据集上进行广泛实验，验证了GraphEdit在不同场景下的有效性与鲁棒性。