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

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 …

Abstract Oversmoothing in Graph Neural Networks (GNNs) refers to the phenomenon where
increasing network depth leads to homogeneous node representations. While previous work …

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 emerged as one of the leading approaches for
machine learning on graph-structured data. Despite their great success, critical …

Graph Neural Networks (GNNs) have succeeded in various computer science applications,
yet deep GNNs underperform their shallow counterparts despite deep learning's success in …

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

Abstract Graph Convolutional Networks (GCNs) are known to suffer from performance
degradation as the number of layers increases, which is usually attributed to over …

Graph neural networks (GNNs) have attracted much attention because of their excellent
performance on tasks such as node classification. However, there is inadequate …

Oversmoothing has been assumed to be the major cause of performance drop in deep
graph convolutional networks (GCNs). In this paper, we propose a new view that deep …