  Graph Neural Networks (GNNs) are powerful in learning semantics of graph
data. Recently, a new paradigm "pre-train and prompt" has shown promising
results in adapting GNNs to various tasks with less supervised data. The
success of such paradigm can be attributed to the more consistent objectives of
pre-training and task-oriented prompt tuning, where the pre-trained knowledge
can be effectively transferred to downstream tasks. Most existing methods are
based on the class prototype vector framework. However, in the few-shot
scenarios, given few labeled data, class prototype vectors are difficult to be
accurately constructed or learned. Meanwhile, the structure information of
graph is usually exploited during pre-training for learning node
representations, while neglected in the prompt tuning stage for learning more
accurate prototype vectors. In addition, they generally ignore the impact of
heterophilous neighborhoods on node representation and are not suitable for
heterophilous graphs. To bridge these gaps, we propose a novel pre-training and
structure prompt tuning framework for GNNs, namely PSP, which consistently
exploits structure information in both pre-training and prompt tuning stages.
In particular, PSP 1) employs a dual-view contrastive learning to align the
latent semantic spaces of node attributes and graph structure, and 2)
incorporates structure information in prompted graph to construct more accurate
prototype vectors and elicit more pre-trained knowledge in prompt tuning. We
conduct extensive experiments on node classification and graph classification
tasks to evaluate the effectiveness of PSP. We show that PSP can lead to
superior performance in few-shot scenarios on both homophilous and
heterophilous graphs. The implemented code is available at
https://github.com/gqq1210/PSP.
