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）与异质图神经网络（HGNNs）是同质与异质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学习范式已广泛流行，但预训练模型与下游任务之间常因目标差异而存在鸿沟。为弥合这一鸿沟，提示学习（prompt learning）在少样本场景中展现出广阔前景，且无需对预训练模型进行全参数微调。尽管图上的提示学习已有初步探索，但这些方法主要处理同质图，忽视了在下游应用中普遍存在的异质图。本文提出HGPROMPT——一种新型预训练与提示框架，不仅通过双模板设计统一了预训练任务与下游任务，更实现了同质图与异质图的统一。此外，我们在HGPROMPT中引入双提示机制，协助下游任务定位最相关的先验知识，从而弥合因特征变异及跨任务异质性差异造成的鸿沟。最终，我们在三个公开数据集上通过大量实验对HGPROMPT进行了全面评估与分析。