In recent years, graph prompt learning/tuning has garnered increasing
attention in adapting pre-trained models for graph representation learning. As
a kind of universal graph prompt learning method, Graph Prompt Feature (GPF)
has achieved remarkable success in adapting pre-trained models for Graph Neural
Networks (GNNs). By fixing the parameters of a pre-trained GNN model, the aim
of GPF is to modify the input graph data by adding some (learnable) prompt
vectors into graph node features to better align with the downstream tasks on
the smaller dataset. However, existing GPFs generally suffer from two main
limitations. First, GPFs generally focus on node prompt learning which ignore
the prompting for graph edges. Second, existing GPFs generally conduct the
prompt learning on all nodes equally which fails to capture the importances of
different nodes and may perform sensitively w.r.t noisy nodes in aligning with
the downstream tasks. To address these issues, in this paper, we propose a new
unified Graph Selective Prompt Feature learning (GSPF) for GNN fine-tuning. The
proposed GSPF integrates the prompt learning on both graph node and edge
together, which thus provides a unified prompt model for the graph data.
Moreover, it conducts prompt learning selectively on nodes and edges by
concentrating on the important nodes and edges for prompting which thus make
our model be more reliable and compact. Experimental results on many benchmark
datasets demonstrate the effectiveness and advantages of the proposed GSPF
method.

近年来，图式提示学习 / 调整在适应预训练模型的图表示学习中引起了越来越多的关注。本文提出了一种新的统一的图选择提示特征学习（GSPF）方法来进行图神经网络的微调，该方法能够集成对图节点和边进行提示学习，并且通过集中关注重要的节点和边来选择性地进行提示学习，从而使得模型更加可靠和紧凑。实验结果表明了所提出的 GSPF 方法的有效性和优势。

图神经网络的统一图选择性提示学习

A Unified Graph Selective Prompt Learning for Graph Neural Networks

Visual prompt tuning (VPT) is a promising solution incorporating learnable
prompt tokens to customize pre-trained models for downstream tasks. However,
VPT and its variants often encounter challenges like prompt initialization,
prompt length, and subpar performance in self-supervised pretraining, hindering
successful contextual adaptation. This study commences by exploring the
correlation evolvement between prompts and patch tokens during proficient
training. Inspired by the observation that the prompt tokens tend to share high
mutual information with patch tokens, we propose initializing prompts with
downstream token prototypes. The strategic initialization, a stand-in for the
previous initialization, substantially improves performance in fine-tuning. To
refine further, we optimize token construction with a streamlined pipeline that
maintains excellent performance with almost no increase in computational
expenses compared to VPT. Exhaustive experiments show our proposed approach
outperforms existing methods by a remarkable margin. For instance, it surpasses
full fine-tuning in 19 out of 24 tasks, using less than 0.4% of learnable
parameters on the FGVC and VTAB-1K benchmarks. Notably, our method
significantly advances the adaptation for self-supervised pretraining,
achieving impressive task performance gains of at least 10% to 30%. Besides,
the experimental results demonstrate the proposed SPT is robust to prompt
lengths and scales well with model capacity and training data size. We finally
provide an insightful exploration into the amount of target data facilitating
the adaptation of pre-trained models to downstream tasks.

通过关键参数的优化和初始化，我们提出了一种改进的视觉提示调整方法，用于优化预训练模型在下游任务中的性能表现，并在大量实验证明该方法在自我监督预训练适应性上取得了显著的性能改进。