Graph neural networks (GNNs) are widely used in machine learning for graph-structured
data. Even though GNNs have achieved remarkable success in real-world applications …

Graph neural networks (GNNs) are effective machine learning models for various graph
learning problems. Despite their empirical successes, the theoretical limitations of GNNs …

Although graph neural networks (GNNs) have made great progress recently on learning
from graph-structured data in practice, their theoretical guarantee on generalizability …

Graph convolutional networks (GCNs) have recently achieved great empirical success in
learning graph-structured data. To address its scalability issue due to the recursive …

In the last decade or so, we have witnessed deep learning reinvigorating the machine
learning field. It has solved many problems in the domains of computer vision, speech …

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

With the development of graph neural networks, how to handle large-scale graph data has
become an increasingly important topic. Currently, most graph neural network models which …

Graph Neural Networks (GNNs) have received considerable attention on graph-structured
data learning for a wide variety of tasks. The well-designed propagation mechanism which …

Graph neural networks (GNNs) are a popular class of machine learning models that have
been successfully applied to a range of problems. Their major advantage lies in their ability …

Recently, Neural Architecture Search (NAS) for GNN has received increasing popularity as it
can seek an optimal architecture for a given new graph. However, the optimal architecture is …