  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.
