Graph neural networks have emerged as a powerful tool for graph representation learning, but their performance heavily relies on abundant task-specific supervision. To reduce labeling requirement, the "pre-train, prompt" paradigms have become increasingly common. However, existing study of prompting on graphs is limited, lacking a universal treatment to appeal to different downstream tasks. In this paper, we propose GraphPrompt, a novel pre-training and prompting framework on graphs. GraphPrompt not only unifies pre-training and downstream tasks into a common task template but also employs a learnable prompt to assist a downstream task in locating the most relevant knowledge from the pre-trained model in a task-specific manner. To further enhance GraphPrompt in these two stages, we extend it into GraphPrompt+ with two major enhancements. First, we generalize several popular graph pre-training tasks beyond simple link prediction to broaden the compatibility with our task template. Second, we propose a more generalized prompt design that incorporates a series of prompt vectors within every layer of the pre-trained graph encoder, in order to capitalize on the hierarchical information across different layers beyond just the readout layer. Finally, we conduct extensive experiments on five public datasets to evaluate and analyze GraphPrompt and GraphPrompt+.

翻译：图神经网络已成为图表示学习的强大工具，但其性能严重依赖于丰富的任务特定监督。为降低标注需求，“预训练-提示”范式日益普遍。然而，现有图提示研究有限，缺乏适用于不同下游任务的通用处理方法。本文提出GraphPrompt——一种新颖的图预训练与提示框架。该框架不仅将预训练与下游任务统一至通用任务模板，还采用可学习提示来辅助下游任务以任务特定的方式从预训练模型中定位最相关知识。为进一步增强这两个阶段的性能，我们将GraphPrompt扩展为GraphPrompt+，其主要改进包括：首先，我们将多种流行的图预训练任务从简单的链路预测推广至更广泛范畴，以提升与任务模板的兼容性；其次，我们提出更广义的提示设计，在预训练图编码器的每一层中融入系列提示向量，从而利用跨不同层级（而不仅是读出层）的层次化信息。最后，我们在五个公共数据集上开展大量实验，对GraphPrompt与GraphPrompt+进行了全面评估与分析。