Enhancing Graph Neural Networks with Structure-Based Prompt
Qingqing Ge Zeyuan Zhao Yiding Liu Anfeng Cheng Xiang Li Shuaiqiang Wang Dawei Yin
Graph Neural Networks (GNNs) are powerful in learning semantics of graph
data. Recently, a new paradigm "pre-train, 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. However, an overlooked
issue of existing studies is that the structure information of graph is usually
exploited during pre-training for learning node representations, while
neglected in the prompt tuning stage for learning task-specific parameters. To
bridge this gap, we propose a novel structure-based prompting method for GNNs,
namely SAP, which consistently exploits structure information in both
pre-training and prompt tuning stages. In particular, SAP 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 elicit more pre-trained knowledge in prompt tuning. We
conduct extensive experiments on node classification and graph classification
tasks to show the effectiveness of SAP. Moreover, we show that SAP can lead to
better performance in more challenging few-shot scenarios on both homophilous
and heterophilous graphs.