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 …

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

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 …

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

Graph-structured data naturally appear in numerous application domains, ranging from
social analysis, bioinformatics to computer vision. The unique capability of graphs enables …

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 …

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 …

In recent years, graph neural networks (GNNs) have emerged as a successful tool in a
variety of graph-related applications. However, the performance of GNNs can be …

Graph deep learning has recently emerged as a powerful ML concept allowing to generalize
successful deep neural architectures to non-euclidean structured data. Such methods have …

Many real data come in the form of non-grid objects, ie graphs, from social networks to
molecules. Adaptation of deep learning from grid-alike data (eg images) to graphs has …