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

Artificial general intelligence on graphs has shown significant advancements
across various applications, yet the traditional 'Pre-train & Fine-tune'
paradigm faces inefficiencies and negative transfer issues, particularly in
complex and few-shot settings. Graph prompt learning emerges as a promising
alternative, leveraging lightweight prompts to manipulate data and fill the
task gap by reformulating downstream tasks to the pretext. However, several
critical challenges still remain: how to unify diverse graph prompt models, how
to evaluate the quality of graph prompts, and to improve their usability for
practical comparisons and selection. In response to these challenges, we
introduce the first comprehensive benchmark for graph prompt learning. Our
benchmark integrates SIX pre-training methods and FIVE state-of-the-art graph
prompt techniques, evaluated across FIFTEEN diverse datasets to assess
performance, flexibility, and efficiency. We also present 'ProG', an
easy-to-use open-source library that streamlines the execution of various graph
prompt models, facilitating objective evaluations. Additionally, we propose a
unified framework that categorizes existing graph prompt methods into two main
approaches: prompts as graphs and prompts as tokens. This framework enhances
the applicability and comparison of graph prompt techniques. The code is
available at: this https URL

人工智能在图上的通用智能在各种应用中取得了显著的进展，然而传统的 “预训练和微调” 范式在复杂和小样本场景下存在低效和负迁移问题。图提示学习作为一种有希望的替代方案出现，利用轻量级提示来操作数据，通过重新构思下游任务来填补任务间隙。然而，仍存在几个关键挑战：如何统一各种图提示模型，如何评估图提示的质量，以及提高其实际应用中的可用性和选择性。为了应对这些挑战，我们推出了图提示学习的首个全面基准。我们的基准集成了六种预训练方法和五种最先进的图提示技术，在十五个多样化的数据集上进行评估，以评估性能、灵活性和效率。我们还介绍了一个名为 'ProG' 的易于使用的开源库，简化了各种图提示模型的执行，有助于客观评估。此外，我们提出了一个统一的框架，将现有的图提示方法分为两个主要方法：图提示和标记提示。该框架增强了图提示技术的适用性和比较性。代码可在此 https URL 中获取。

ProG：图灵提示学习基准

ProG: A Graph Prompt Learning Benchmark

Graph Prompt Learning (GPL) bridges significant disparities between
pretraining and downstream applications to alleviate the knowledge transfer
bottleneck in real-world graph learning. While GPL offers superior
effectiveness in graph knowledge transfer and computational efficiency, the
security risks posed by backdoor poisoning effects embedded in pretrained
models remain largely unexplored. Our study provides a comprehensive analysis
of GPL's vulnerability to backdoor attacks. We introduce \textit{CrossBA}, the
first cross-context backdoor attack against GPL, which manipulates only the
pretraining phase without requiring knowledge of downstream applications. Our
investigation reveals both theoretically and empirically that tuning trigger
graphs, combined with prompt transformations, can seamlessly transfer the
backdoor threat from pretrained encoders to downstream applications. Through
extensive experiments involving 3 representative GPL methods across 5 distinct
cross-context scenarios and 5 benchmark datasets of node and graph
classification tasks, we demonstrate that \textit{CrossBA} consistently
achieves high attack success rates while preserving the functionality of
downstream applications over clean input. We also explore potential
countermeasures against \textit{CrossBA} and conclude that current defenses are
insufficient to mitigate \textit{CrossBA}. Our study highlights the persistent
backdoor threats to GPL systems, raising trustworthiness concerns in the
practices of GPL techniques.

通过实验证明了 Graph Prompt Learning（GPL）系统的持续背门威胁，引发了 GPL 技术实践中的可靠性问题。