Graph neural networks (GNNs) and heterogeneous graph neural networks (HGNNs) are prominent techniques for homogeneous and heterogeneous graph representation learning, yet their performance in an end-to-end supervised framework greatly depends on the availability of task-specific supervision. To reduce the labeling cost, pre-training on self-supervised pretext tasks has become a popular paradigm,but there is often a gap between the pre-trained model and downstream tasks, stemming from the divergence in their objectives. To bridge the gap, prompt learning has risen as a promising direction especially in few-shot settings, without the need to fully fine-tune the pre-trained model. While there has been some early exploration of prompt-based learning on graphs, they primarily deal with homogeneous graphs, ignoring the heterogeneous graphs that are prevalent in downstream applications. In this paper, we propose HGPROMPT, a novel pre-training and prompting framework to unify not only pre-training and downstream tasks but also homogeneous and heterogeneous graphs via a dual-template design. Moreover, we propose dual-prompt in HGPROMPT to assist a downstream task in locating the most relevant prior to bridge the gaps caused by not only feature variations but also heterogeneity differences across tasks. Finally, we thoroughly evaluate and analyze HGPROMPT through extensive experiments on three public datasets.

翻译：图神经网络（GNNs）与异构图神经网络（HGNNs）是同构与异构图表示学习的代表性技术，然而它们在端到端监督框架下的性能高度依赖于任务特定标注数据的可用性。为降低标注成本，基于自监督预训练任务的方法已成为主流范式，但预训练模型与下游任务之间常因目标差异存在语义鸿沟。为弥合这一鸿沟，提示学习成为有前景的方向——尤其在少样本场景中，无需对预训练模型进行完整微调。尽管已有图数据上基于提示学习的初步探索，但这些方法主要处理同构图，忽略了在下游应用中普遍存在的异构图。本文提出HGPROMPT——一种新型预训练与提示框架，通过双模板设计不仅统一了预训练与下游任务，更实现了同构图与异构图间的统一。进一步地，我们在HGPROMPT中设计双提示机制，协助下游任务定位最相关先验知识，以弥合由特征差异及任务间异质性差异引发的双重鸿沟。最后，我们在三个公开数据集上通过大量实验对HGPROMPT进行全面评估与分析。