  Graph Neural Networks (GNNs) have become widely-used models for
semi-supervised learning. However, the robustness of GNNs in the presence of
label noise remains a largely under-explored problem. In this paper, we
consider an important yet challenging scenario where labels on nodes of graphs
are not only noisy but also scarce. In this scenario, the performance of GNNs
is prone to degrade due to label noise propagation and insufficient learning.
To address these issues, we propose a novel RTGNN (Robust Training of Graph
Neural Networks via Noise Governance) framework that achieves better robustness
by learning to explicitly govern label noise. More specifically, we introduce
self-reinforcement and consistency regularization as supplemental supervision.
The self-reinforcement supervision is inspired by the memorization effects of
deep neural networks and aims to correct noisy labels. Further, the consistency
regularization prevents GNNs from overfitting to noisy labels via mimicry loss
in both the inter-view and intra-view perspectives. To leverage such
supervisions, we divide labels into clean and noisy types, rectify inaccurate
labels, and further generate pseudo-labels on unlabeled nodes. Supervision for
nodes with different types of labels is then chosen adaptively. This enables
sufficient learning from clean labels while limiting the impact of noisy ones.
We conduct extensive experiments to evaluate the effectiveness of our RTGNN
framework, and the results validate its consistent superior performance over
state-of-the-art methods with two types of label noises and various noise
rates.
