  While robust graph neural networks (GNNs) have been widely studied for graph
perturbation and attack, those for label noise have received significantly less
attention. Most existing methods heavily rely on the label smoothness
assumption to correct noisy labels, which adversely affects their performance
on heterophilous graphs. Further, they generally perform poorly in high
noise-rate scenarios. To address these problems, in this paper, we propose a
novel probabilistic graphical model (PGM) based framework LNP. Given a noisy
label set and a clean label set, our goal is to maximize the likelihood of
labels in the clean set. We first present LNP-v1, which generates clean labels
based on graphs only in the Bayesian network. To further leverage the
information of clean labels in the noisy label set, we put forward LNP-v2,
which incorporates the noisy label set into the Bayesian network to generate
clean labels. The generative process can then be used to predict labels for
unlabeled nodes. We conduct extensive experiments to show the robustness of LNP
on varying noise types and rates, and also on graphs with different
heterophilies. In particular, we show that LNP can lead to inspiring
performance in high noise-rate situations.
