无需重训练，用轻量模型为预训练GNN提供可信度量化。
Evidential Uncertainty Probes for Graph Neural Networks
- 用轻量MLP从预训练表示中提取证据，实现即插即用的不确定性估计。
- 提出EPN-reg正则化方法，在多个数据集上显著提升认知不确定性估计精度。
- 适合高风险场景如药物发现、金融反欺诈中的可靠部署需求。
在药物发现和金融欺诈检测等高风险应用中，准确量化认知不确定性和随机不确定性对图神经网络（GNN）的可靠部署至关重要。尽管证据深度学习（EDL）通过狄利克雷分布对预测概率进行不确定性建模，现有基于EDL的GNN（EGNN）模型需修改网络结构并重新训练，无法直接利用预训练模型。本文提出一种即插即用的框架，可在不重训练的前提下实现GNN的不确定性量化。所提证据探测网络（EPN）采用轻量级多层感知机（MLP）头，从学习到的表示中提取证据，可高效集成于多种GNN架构。进一步引入基于证据的正则化技术（EPN-reg），在理论上支持更优的认知不确定性估计。大量实验表明，EPN-reg在准确性和效率上均达到当前最优水平，适用于实际部署。
原文摘要 · Abstract (English)
Accurate quantification of both aleatoric and epistemic uncertainties is essential when deploying Graph Neural Networks (GNNs) in high-stakes applications such as drug discovery and financial fraud detection, where reliable predictions are critical. Although Evidential Deep Learning (EDL) efficiently quantifies uncertainty using a Dirichlet distribution over predictive probabilities, existing EDL-based GNN (EGNN) models require modifications to the network architecture and retraining, failing to take advantage of pre-trained models. We propose a plug-and-play framework for uncertainty quantification in GNNs that works with pre-trained models without the need for retraining. Our Evidential Probing Network (EPN) uses a lightweight Multi-Layer-Perceptron (MLP) head to extract evidence from learned representations, allowing efficient integration with various GNN architectures. We further introduce evidence-based regularization techniques, referred to as EPN-reg, to enhance the estimation of epistemic uncertainty with theoretical justifications. Extensive experiments demonstrate that the proposed EPN-reg achieves state-of-the-art performance in accurate and efficient uncertainty quantification, making it suitable for real-world deployment.
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