  Graph neural networks (GNNs) have achieved state-of-the-art performance for
node classification on graphs. The vast majority of existing works assume that
genuine node labels are always provided for training. However, there has been
very little research effort on how to improve the robustness of GNNs in the
presence of label noise. Learning with label noise has been primarily studied
in the context of image classification, but these techniques cannot be directly
applied to graph-structured data, due to two major challenges -- label sparsity
and label dependency -- faced by learning on graphs. In this paper, we propose
a new framework, UnionNET, for learning with noisy labels on graphs under a
semi-supervised setting. Our approach provides a unified solution for robustly
training GNNs and performing label correction simultaneously. The key idea is
to perform label aggregation to estimate node-level class probability
distributions, which are used to guide sample reweighting and label correction.
Compared with existing works, UnionNET has two appealing advantages. First, it
requires no extra clean supervision, or explicit estimation of the noise
transition matrix. Second, a unified learning framework is proposed to robustly
train GNNs in an end-to-end manner. Experimental results show that our proposed
approach: (1) is effective in improving model robustness against different
types and levels of label noise; (2) yields significant improvements over
state-of-the-art baselines.
