Evidential Uncertainty Probes for Graph Neural Networks

• URL: http://arxiv.org/abs/2503.08097v1
• Date: Tue, 11 Mar 2025 07:00:54 GMT
• Title: Evidential Uncertainty Probes for Graph Neural Networks
• Authors: Linlin Yu, Kangshuo Li, Pritom Kumar Saha, Yifei Lou, Feng Chen,
• Abstract summary: We propose a plug-and-play framework for uncertainty quantification in Graph Neural Networks (GNNs)<n>Our Evidential Probing Network (EPN) uses a lightweight Multi-Layer-Perceptron (MLP) head to extract evidence from learned representations.<n>EPN-reg achieves state-of-the-art performance in accurate and efficient uncertainty quantification, making it suitable for real-world deployment.
• Score: 3.5169632430086315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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