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.

翻译：图上的节点分类是一项重要任务，广泛应用于社交分析和异常检测等领域。尽管图神经网络（GNN）在这一任务上取得了显著成果，但现有技术通常假设节点的标签信息是准确的，而在现实应用中这一假设可能不成立。为解决此问题，我们研究了带有标签噪声的图学习问题，并提出了一种名为一致性图神经网络（CGNN）的新方法。具体而言，我们将图对比学习作为正则化项，促使增强后的节点两个视图具有一致的表示。由于该正则化项无法利用标签信息，因而能够增强节点表示对标签噪声的鲁棒性。此外，为检测图上的噪声标签，我们基于同质性假设提出了一种样本选择技术，通过衡量标签与其邻居节点之间的一致性来识别噪声节点。最后，我们对这些置信的噪声标签进行净化，以实现高效的语义图学习。在三个广泛使用的基准数据集上的大量实验表明，我们的CGNN方法优于现有竞争方法。