In recent years, prompt tuning has set off a research boom in the adaptation
of pre-trained models. In this paper, we propose Graph Prompt as an efficient
and effective alternative to full fine-tuning for adapting the pre-trianed GNN
models to downstream tasks. To the best of our knowledge, we are the first to
explore the effectiveness of prompt tuning on existing pre-trained GNN models.
Specifically, without tuning the parameters of the pre-trained GNN model, we
train a task-specific graph prompt that provides graph-level transformations on
the downstream graphs during the adaptation stage. Then, we introduce a
concrete implementation of the graph prompt, called GP-Feature (GPF), which
adds learnable perturbations to the feature space of the downstream graph. GPF
has a strong expressive ability that it can modify both the node features and
the graph structure implicitly. Accordingly, we demonstrate that GPF can
achieve the approximately equivalent effect of any graph-level transformations
under most existing pre-trained GNN models. We validate the effectiveness of
GPF on numerous pre-trained GNN models, and the experimental results show that
with a small amount (about 0.1% of that for fine-tuning ) of tunable
parameters, GPF can achieve comparable performances as fine-tuning, and even
obtain significant performance gains in some cases.

本研究探索了 Graph Prompt 作为预训练 GNN 模型适应下游任务的有效替代方法，通过对下游图形的图形级转换进行任务特定的图形提示来实现，从而实现与微调相当甚至在某些情况下获得显著性能提升。