arXiv Paper Proposes Online Bayesian Node Classification for Evolving Graphs

A new arXiv preprint addresses node classification on evolving graphs, where classifiers must generalize to newly arriving nodes despite distribution shift while also providing calibrated uncertainty. The authors propose an online Bayesian approach aimed at inductive settings where safety-sensitive applications require trustworthy confidence estimates. The work targets a gap left by standard graph neural networks, which typically assume static graphs and offer limited uncertainty quantification.