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上相互连接的实体，从而支撑一系列Web应用，如网页分析与内容推荐。近年来，图神经网络（GNNs）已成为图表示学习的主流技术。然而，在端到端监督框架中，其有效性严重依赖于任务特定标签的可用性。为降低标记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一种有前景的方法，而提示学习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。尽管已有一些针对图的提示学习初步探索，但它们主要利用单一预训练任务，导致仅能从预训练数据中学习到有限的通用知识。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获取更全面的预训练知识。首先，在预训练阶段，我们设计了一组预训练令牌以协同多个预训练任务。其次，我们提出了一种包含组合提示与开放提示的双重提示机制，以利用任务特定与全局预训练知识，引导少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大量实验，以评估与分析MultiGPrompt。