Adaptive Dual Prompting: Hierarchical Debiasing for Fairness-aware Graph Neural Networks

ICLR 2026 Conference Submission18178 Authors

Keywords: Graph Neural Networks, Fairness, Prompt Learning

TL;DR: An Adaptive Dual Prompting method for Hierarchical Debiasing in Graph Neural Networks.

Abstract: 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 produces discriminative node representations across demographic subgroups because the downstream graph data itself contains inherent 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 in graph prompting, we design an Adaptive Feature Rectification module that learns customized attribute prompts to suppress sensitive information via a self-gating mechanism at the input layer, thereby reducing biased inputs at the source. Afterward, we propose an Adaptive Message Calibration module that generates structure prompts at each layer, which adjust the messages from neighboring nodes to enable dynamic and soft calibration of the information flow. In the end, ADPrompt optimizes these two prompting modules using a joint optimization objective for adapting the pre-trained GNN model while enhancing model 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.

Primary Area: learning on graphs and other geometries & topologies

Submission Number: 18178