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.

我们的研究旨在提高图模型在具有挑战性的零样本学习场景中的泛化能力，通过发展面向图结构知识的大型语言模型（LLMs），并利用图指示调整范式来与图结构知识建立联系，探索自我监督的图结构信号和任务特定的图指示，从而引导 LLMs 在理解复杂的图结构和改善在不同下游任务之间的适应性。我们的框架在监督和零样本图学习任务上进行评估，展示了优越的泛化能力，并超过了最先进的基准模型。