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：图能够天然地建模Web上的互联对象，从而支持一系列Web应用，如网络分析和内容推荐。近年来，图神经网络（Graph Neural Networks, GNNs）已成为图表示学习的主流技术。然而，在端到端监督框架下，其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小样本场景下的鲁棒性，基于自监督任务的预训练已成为一种有前景的方法，而提示学习（prompting）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距。尽管已有初步探索将提示学习应用于图上，但这些方法主要依赖单一预训练任务，导致从预训练数据中学习到的通用知识较为有限。为此，本文提出MultiGPrompt——一种新颖的多任务预训练与提示学习框架，通过利用多个预训练任务来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，我们提出由组合提示（composed prompts）和开放提示（open prompts）组成的双提示机制，分别利用任务特定知识和全局预训练知识，以指导小样本场景下的下游任务。最后，我们在六个公开数据集上进行了广泛实验，以评估和分析MultiGPrompt的性能。