Deep neural networks have achieved significant success in the last decades,
but they are not well-calibrated and often produce unreliable predictions. A
large number of literature relies on uncertainty quantification to evaluate the
reliability of a learning model, which is particularly important for
applications of out-of-distribution (OOD) detection and misclassification
detection. We are interested in uncertainty quantification for interdependent
node-level classification. We start our analysis based on graph posterior
networks (GPNs) that optimize the uncertainty cross-entropy (UCE)-based loss
function. We describe the theoretical limitations of the widely-used UCE loss.
To alleviate the identified drawbacks, we propose a distance-based
regularization that encourages clustered OOD nodes to remain clustered in the
latent space. We conduct extensive comparison experiments on eight standard
datasets and demonstrate that the proposed regularization outperforms the
state-of-the-art in both OOD detection and misclassification detection.

深度神经网络在过去几十年取得了重大成功，但它们没有很好的校准性，经常产生不可靠的预测。本文关注于不确定性量化在依赖节点级分类中的应用，并提出了改进的正则化方法来提高分类模型的可靠性。经过大量实验证明，在超过分布检测和误分类检测的应用场景中，提出的正则化方法优于目前的最新技术。