  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.
