Graphs naturally appear in numerous application domains, ranging from social analysis,
bioinformatics to computer vision. The unique capability of graphs enables capturing the …

Multi-view learning (MVL) has attracted increasing attention and achieved great practical
success by exploiting complementary information of multiple features or modalities …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

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 …

Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-
structured data. Recently, GCNs and subsequent variants have shown superior performance …

State-of-the-art Graph Neural Networks (GNNs) have limited scalability with respect to the
graph and model sizes. On large graphs, increasing the model depth often means …

\emph {Over-fitting} and\emph {over-smoothing} are two main obstacles of developing deep
Graph Convolutional Networks (GCNs) for node classification. In particular, over-fitting …

Graph Convolutional Networks (GCNs) are powerful models for learning representations of
attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer …

Abstract Graph Neural Networks (GNNs) are a powerful tool for machine learning on graphs.
GNNs combine node feature information with the graph structure by recursively passing …

Deep learning has revolutionized many machine learning tasks in recent years, ranging
from image classification and video processing to speech recognition and natural language …