Graph Neural Networks (GNNs) have advanced graph structure understanding via recursive information exchange and aggregation among graph nodes. To improve model robustness, self-supervised learning (SSL) has emerged as a promising approach for data augmentation. However, existing methods for generating pre-trained graph embeddings often rely on fine-tuning with specific downstream task labels, which limits their usability in scenarios where labeled data is scarce or unavailable. To address this, our research focuses on advancing the generalization capabilities of graph models in challenging zero-shot learning scenarios. Inspired by the success of large language models (LLMs), we aim to develop a graph-oriented LLM that can achieve high generalization across diverse downstream datasets and tasks, even without any information available from the downstream graph data. In this work, we present the GraphGPT framework that aligns LLMs with graph structural knowledge with a graph instruction tuning paradigm. Our framework incorporates a text-graph grounding component to establish a connection between textual information and graph structures. Additionally, we propose a dual-stage instruction tuning paradigm, accompanied by a lightweight graph-text alignment projector. This paradigm explores self-supervised graph structural signals and task-specific graph instructions, to guide LLMs in understanding complex graph structures and improving their adaptability across different downstream tasks. Our framework is evaluated on supervised and zero-shot graph learning tasks, demonstrating superior generalization and outperforming state-of-the-art baselines.

翻译：图神经网络（GNNs）通过图节点间的递归信息交换与聚合技术，推动了图结构理解领域的发展。为提升模型鲁棒性，自监督学习（SSL）已成为一种颇具前景的数据增强手段。然而，现有生成预训练图嵌入的方法通常依赖特定下游任务标签进行微调，这在标注数据稀缺或不可用的场景中限制了其适用性。为解决该问题，本研究聚焦于提升图模型在具有挑战性的零样本学习场景中的泛化能力。受大语言模型（LLMs）成功经验的启发，我们致力于开发一种面向图的LLM，使其能够在完全无下游图数据信息的情况下，实现跨不同下游数据集与任务的高泛化性能。本文提出GraphGPT框架，该框架通过图指令微调范式实现LLMs与图结构知识的对齐。我们设计了一个文本-图对齐组件，用于建立文本信息与图结构间的关联；同时提出了一种双阶段指令微调范式，并配套开发了轻量级图文对齐投影器。该范式通过探索自监督图结构信号与任务特定图指令，引导LLMs理解复杂图结构并提升其跨不同下游任务的适应能力。在监督与零样本图学习任务上的评估结果表明，本框架展现出优越的泛化性能，全面超越了现有最优基线模型。