Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation. While graph neural networks(GNNs) have emerged as a powerful tool for graph representation learning, in an end-to-end supervised setting, their performance heavily rely on a large amount of task-specific supervision. To reduce labeling requirement, the "pre-train, fine-tune" and "pre-train, prompt" paradigms have become increasingly common. In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner. However, existing study of prompting on graphs is still 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-train model in a task-specific manner. Finally, we conduct extensive experiments on five public datasets to evaluate and analyze GraphPrompt.

翻译：图能够建模对象之间的复杂关系，支撑着诸如在线页面/文章分类和社交推荐等众多网络应用。尽管图神经网络（GNN）已成为一种强大的图表示学习工具，但在端到端监督学习场景下，其性能严重依赖于大量任务特定的监督信号。为降低标注需求，“预训练-微调”与“预训练-提示”范式日益常见。其中，提示学习作为自然语言处理中微调的热门替代方案，旨在以任务特定方式缩小预训练目标与下游目标之间的差距。然而，现有关于图提示学习的研究仍十分有限，缺乏适用于不同下游任务的通用方法。本文提出GraphPrompt——一种新颖的图预训练与提示学习框架。GraphPrompt不仅将预训练和下游任务统一到通用任务模板中，还通过可学习提示，以任务特定方式辅助下游任务从预训练模型中定位最相关的知识。最后，我们在五个公开数据集上开展大量实验，对GraphPrompt进行评测与分析。