Learning on Graphs under Label Noise
Jingyang Yuan Xiao Luo Yifang Qin Yusheng Zhao Wei Ju Ming Zhang
Node classification on graphs is a significant task with a wide range of
applications, including social analysis and anomaly detection. Even though
graph neural networks (GNNs) have produced promising results on this task,
current techniques often presume that label information of nodes is accurate,
which may not be the case in real-world applications. To tackle this issue, we
investigate the problem of learning on graphs with label noise and develop a
novel approach dubbed Consistent Graph Neural Network (CGNN) to solve it.
Specifically, we employ graph contrastive learning as a regularization term,
which promotes two views of augmented nodes to have consistent representations.
Since this regularization term cannot utilize label information, it can enhance
the robustness of node representations to label noise. Moreover, to detect
noisy labels on the graph, we present a sample selection technique based on the
homophily assumption, which identifies noisy nodes by measuring the consistency
between the labels with their neighbors. Finally, we purify these confident
noisy labels to permit efficient semantic graph learning. Extensive experiments
on three well-known benchmark datasets demonstrate the superiority of our CGNN
over competing approaches.