Graph classification is a fundamental problem with diverse applications in bioinformatics
and chemistry. Due to the intricate procedures of manual annotations in graphical domains …

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 …

Graph Neural Network (GNN) has emerged as a predominant tool for graph data analysis.
Despite their proliferation, the low-quality labels of many real-world graphs will undermine …

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 …

Graph Neural Networks (GNNs) have achieved promising results for semi-supervised
learning tasks on graphs such as node classification. Despite the great success of GNNs …

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 …

Graph classification is a critical research problem in many applications from different
domains. In order to learn a graph classification model, the most widely used supervision …

Graph Neural Networks (GNNs) have achieved unprecedented success in identifying
categorical labels of graphs. However, most existing graph classification problems with …

This work develops mixup for graph data. Mixup has shown superiority in improving the
generalization and robustness of neural networks by interpolating features and labels …

We present a simple and yet effective interpolation-based regularization technique, aiming
to improve the generalization of Graph Neural Networks (GNNs) on supervised graph …