Recent years have witnessed fast developments of graph neural networks (GNNs) that have
benefited myriads of graph analytic tasks and applications. In general, most GNNs depend …

Abstract Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using
graph structures based on the relational inductive bias (homophily assumption). While …

Node classification is a classical graph representation learning task on which Graph Neural
Networks (GNNs) have recently achieved strong results. However, it is often believed that …

In node classification tasks, graph convolutional neural networks (GCNs) have
demonstrated competitive performance over traditional methods on diverse graph data …

Abstract Recent studies on Graph Neural Networks (GNNs) provide both empirical and
theoretical evidence supporting their effectiveness in capturing structural patterns on both …

Homophily principle, ie, nodes with the same labels are more likely to be connected, has
been believed to be the main reason for the performance superiority of Graph Neural …

Graph Neural Networks (GNNs) have risen to prominence in learning representations for
graph structured data. A single GNN layer typically consists of a feature transformation and a …

Graph anomaly detection (GAD) suffers from heterophily—abnormal nodes are sparse so
that they are connected to vast normal nodes. The current solutions upon Graph Neural …

Graph neural networks (GNNs) based methods have achieved impressive performance on
node clustering task. However, they are designed on the homophilic assumption of graph …

Graph Neural Networks (GNNs) are widely used on a variety of graph-based machine
learning tasks. For node-level tasks, GNNs have strong power to model the homophily …