Graphs can inherently model interconnected objects on the Web, thereby facilitating a series of Web applications, such as web analyzing and content recommendation. Recently, Graph Neural Networks (GNNs) have emerged as a mainstream technique for graph representation learning. However, their efficacy within an end-to-end supervised framework is significantly tied to the availabilityof task-specific labels. To mitigate labeling costs and enhance robustness in few-shot settings, pre-training on self-supervised tasks has emerged as a promising method, while prompting has been proposed to further narrow the objective gap between pretext and downstream tasks. Although there has been some initial exploration of prompt-based learning on graphs, they primarily leverage a single pretext task, resulting in a limited subset of general knowledge that could be learned from the pre-training data. Hence, in this paper, we propose MultiGPrompt, a novel multi-task pre-training and prompting framework to exploit multiple pretext tasks for more comprehensive pre-trained knowledge. First, in pre-training, we design a set of pretext tokens to synergize multiple pretext tasks. Second, we propose a dual-prompt mechanism consisting of composed and open prompts to leverage task-specific and global pre-training knowledge, to guide downstream tasks in few-shot settings. Finally, we conduct extensive experiments on six public datasets to evaluate and analyze MultiGPrompt.

翻译：图结构天然能够建模Web上互联的对象，从而支撑起一系列Web应用，如网页分析与内容推荐。近年来，图神经网络（GNN）已成为图表示学习的主流技术。然而，其在端到端监督框架中的有效性高度依赖于任务特定标签的可用性。为降低标注成本并增强少样本场景下的鲁棒性，基于自监督任务的预训练成为有前景的方法，而提示（Prompting）技术则被提出以进一步缩小预训练任务与下游任务之间的目标差异。尽管已有初步的图提示学习探索，但这些方法主要利用单一预训练任务，导致只能从预训练数据中学习到有限的一般性知识。为此，本文提出MultiGPrompt——一种新颖的多任务预训练与提示框架，通过利用多个预训练任务获取更全面的预训练知识。首先，在预训练阶段，我们设计一组预训练令牌（Pretext Tokens）以协同多个预训练任务；其次，提出由组合式提示（Composed Prompt）和开放式提示（Open Prompt）组成的双提示机制，分别利用任务特定知识与全局预训练知识，在少样本场景下引导下游任务；最后，在六个公开数据集上进行大量实验，以评估和分析MultiGPrompt的性能。