Graph Neural Networks (GNNs) have shown their great ability in modeling graph structured
data. However, real-world graphs usually contain structure noises and have limited labeled …

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 …

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 shown to be powerful tools for graph analytics. The
key idea is to recursively propagate and aggregate information along the edges of the given …

Graph Neural Networks (GNNs) have exhibited impressive performance in many graph
learning tasks. Nevertheless, the performance of GNNs can deteriorate when the input …

Graph neural networks (GNNs) have achieved state-of-the-art results for semi-supervised
node classification on graphs. Nevertheless, the challenge of how to effectively learn GNNs …

Graph neural networks (GNNs) have shown the power in graph representation learning for
numerous tasks. In this work, we discover an interesting phenomenon that although residual …

Node classification is a fundamental graph-based task that aims to predict the classes of
unlabeled nodes, for which Graph Neural Networks (GNNs) are the state-of-the-art methods …

Graph neural networks (GNNs) have emerged due to their success at modeling graph data.
Yet, it is challenging for GNNs to efficiently scale to large graphs. Thus, distributed GNNs …