Data augmentation has recently seen increased interest in graph machine learning given its
demonstrated ability to improve model performance and generalization by added training …

Graph neural networks, a powerful deep learning tool to model graph-structured data, have
demonstrated remarkable performance on numerous graph learning tasks. To address the …

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 …

Recently, there has been an increasing interest in (supervised) learning with graph data,
especially using graph neural networks. However, the development of meaningful …

Large-scale graph training is a notoriously challenging problem for graph neural networks
(GNNs). Due to the nature of evolving graph structures into the training process, vanilla …

Graph-structured datasets usually have irregular graph sizes and connectivities, rendering
the use of recent data augmentation techniques, such as Mixup, difficult. To tackle this …

Graph Neural Networks (GNNs), which generalize deep neural networks to graph-structured
data, have drawn considerable attention and achieved state-of-the-art performance in …

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 …

In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative
and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph …

Graph neural networks have emerged as a leading architecture for many graph-level tasks,
such as graph classification and graph generation. As an essential component of the …