In recent years, pre-training Graph Neural Networks (GNNs) through self-supervised learning on unlabeled graph data has emerged as a widely adopted paradigm in graph learning. Although the paradigm is effective for pre-training powerful GNN models, the objective gap often exists between pre-training and downstream tasks. To bridge this gap, graph prompting adapts pre-trained GNN models to specific downstream tasks with extra learnable prompts while keeping the pre-trained GNN models frozen. As recent graph prompting methods largely focus on enhancing model utility on downstream tasks, they often overlook fairness concerns when designing prompts for adaptation. In fact, pre-trained GNN models will produce discriminative node representations across demographic subgroups, as downstream graph data inherently contains biases in both node attributes and graph structures. To address this issue, we propose an Adaptive Dual Prompting (ADPrompt) framework that enhances fairness for adapting pre-trained GNN models to downstream tasks. To mitigate attribute bias, we design an Adaptive Feature Rectification module that learns customized attribute prompts to suppress sensitive information at the input layer, reducing bias at the source. Afterward, we propose an Adaptive Message Calibration module that generates structure prompts at each layer, which adjust the message from neighboring nodes to enable dynamic and soft calibration of the information flow. Finally, ADPrompt jointly optimizes the two prompting modules to adapt the pre-trained GNN while enhancing fairness. We conduct extensive experiments on four datasets with four pre-training strategies to evaluate the performance of ADPrompt. The results demonstrate that our proposed ADPrompt outperforms seven baseline methods on node classification tasks.

翻译：近年来，通过自监督学习在未标记图数据上预训练图神经网络已成为图学习领域广泛采用的范式。尽管该范式能有效预训练强大的GNN模型，但预训练与下游任务之间常存在目标差异。为弥合此差异，图提示技术通过引入额外可学习的提示参数，使预训练GNN模型适配特定下游任务，同时保持预训练模型参数冻结。现有图提示方法主要聚焦于提升下游任务的模型效用，在设计适配提示时往往忽视公平性问题。实际上，由于下游图数据在节点属性和图结构层面均存在固有偏差，预训练GNN模型会产生跨人口统计子群的歧视性节点表征。为解决此问题，我们提出自适应双重提示框架，在适配预训练GNN至下游任务时增强公平性。为缓解属性偏差，我们设计了自适应特征校正模块，通过学习定制化的属性提示在输入层抑制敏感信息，从源头减少偏差。随后，我们提出自适应消息校准模块，在每一层生成结构提示，通过调整相邻节点的信息传递实现信息流的动态软校准。最终，ADPrompt联合优化两个提示模块，在适配预训练GNN的同时提升公平性。我们在四个数据集上采用四种预训练策略开展广泛实验，评估ADPrompt的性能。结果表明，在节点分类任务中，我们提出的ADPrompt优于七种基线方法。