Graph neural networks (GNNs) have shown state-of-the-art performances in various
applications. However, GNNs often struggle to capture long-range dependencies in graphs …

Node features of graph neural networks (GNNs) tend to become more similar with the
increase of the network depth. This effect is known as over-smoothing, which we …

Due to the over-smoothing issue, most existing graph neural networks can only capture
limited dependencies with their inherently finite aggregation layers. To overcome this …

Oversmoothing is a central challenge of building more powerful Graph Neural Networks
(GNNs). While previous works have only demonstrated that oversmoothing is inevitable …

Graph neural networks (GNNs) are powerful models to handle graph data and can achieve
state-of-the-art in many critical tasks including node classification and link prediction …

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

Increasing the depth of GCN, which is expected to permit more expressivity, is shown to
incur performance detriment especially on node classification. The main cause of this lies in …

Graph neural networks (GNNs) have been proven to be effective in various network-related
tasks. Most existing GNNs usually exploit the low-frequency signals of node features, which …

Graph neural networks (GNNs) are processing architectures that exploit graph structural
information to model representations from network data. Despite their success, GNNs suffer …

Graph neural networks (GNNs), which learn the representation of a node by aggregating its
neighbors, have become an effective computational tool in downstream applications. Over …