Noise-resistant graph neural networks with manifold consistency and label consistency

Abstract

Previous graph neural networks (GNNs) consider the consistency in either the feature space or the label space to deal with the issue of noisy labels by ignoring their complementary information. To address this issue, in this paper, we propose a new method to simultaneously consider them in a unified framework. To do this, we design a manifold regularization to enable the embeddings of every node and its neighbors similar. Moreover, we also design a label consistency regularization to make pseudo labels of augmentation data consistent to predicted labels of unlabeled nodes, as well as make predicted labels of every unlabeled node consistent to pseudo labels of its neighborhood. Furthermore, we prove that these two regularizations provide complementary information to each other for handling label noise. Finally, experimental results on multiple real-world datasets demonstrate that our proposed method achieves superior results in terms of node classification with different label noise ratios, compared to SOTA methods.