With graphs rapidly growing in size and deeper graph neural networks (GNNs) emerging,
the training and inference of GNNs become increasingly expensive. Existing network weight …

The training and inference of Graph Neural Networks (GNNs) are costly when scaling up to
large-scale graphs. Graph Lottery Ticket (GLT) has presented the first attempt to accelerate …

Training Graph Neural Networks (GNNs) on large graphs is a fundamental challenge due to
the high memory usage, which is mainly occupied by activations (eg, node embeddings) …

Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art graph learning
model. However, it can be notoriously challenging to inference GCNs over large graph …

Graph neural networks (GNNs) tend to suffer from high computation costs due to the
exponentially increasing scale of graph data and a large number of model parameters …

Graph Neural Networks (GNNs) have shown great promise in various graph learning tasks.
However, the computational overheads of fitting GNNs to large-scale graphs grow rapidly …

Graph neural networks (GNNs) have been a hot spot of recent research and are widely
utilized in diverse applications. However, with the use of huger data and deeper models, an …

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

Molecular property prediction is an important direction in computer-aided drug design. In this
paper, to fully explore the information from SMILE stings and graph data of molecules, we …

The history of artificial intelligence (AI) has witnessed the significant impact of high-quality
data on various deep learning models, such as ImageNet for AlexNet and ResNet. Recently …