Graph Neural Networks (GNNs) have become essential in interpreting relational data across various domains, yet, they often struggle to generalize to unseen graph data that differs markedly from training instances. In this paper, we introduce a novel framework called General Retrieval-Augmented Graph Learning (RAGraph), which brings external graph data into the general graph foundation model to improve model generalization on unseen scenarios. On the top of our framework is a toy graph vector library that we established, which captures key attributes, such as features and task-specific label information. During inference, the RAGraph adeptly retrieves similar toy graphs based on key similarities in downstream tasks, integrating the retrieved data to enrich the learning context via the message-passing prompting mechanism. Our extensive experimental evaluations demonstrate that RAGraph significantly outperforms state-of-the-art graph learning methods in multiple tasks such as node classification, link prediction, and graph classification across both dynamic and static datasets. Furthermore, extensive testing confirms that RAGraph consistently maintains high performance without the need for task-specific fine-tuning, highlighting its adaptability, robustness, and broad applicability.

翻译：图神经网络（GNNs）已成为解析各领域关系数据的关键工具，但其在处理与训练实例显著不同的未见图数据时，泛化能力往往不足。本文提出了一种名为通用检索增强图学习（RAGraph）的新框架，该框架将外部图数据引入通用图基础模型，以提升模型在未见场景下的泛化性能。我们构建了一个玩具图向量库作为框架的核心组件，该库捕获了关键属性，如特征和任务特定的标签信息。在推理过程中，RAGraph能够基于下游任务中的关键相似性，熟练地检索出相似的玩具图，并通过消息传递提示机制整合检索到的数据，以丰富学习上下文。我们广泛的实验评估表明，在动态和静态数据集上的节点分类、链接预测和图分类等多个任务中，RAGraph显著优于当前最先进的图学习方法。此外，大量测试证实，RAGraph无需针对特定任务进行微调即可持续保持高性能，突显了其适应性、鲁棒性和广泛的适用性。