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 this paper, we propose an end-to-end graph learning framework, namely\textbf {I}
terative\textbf {D} eep\textbf {G} raph\textbf {L} earning (\alg), for jointly and iteratively …

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 neural networks (GNNs) demonstrate outstanding performance in a broad range of
applications. While the majority of GNN applications assume that a graph structure is given …

Graph neural networks (GNNs) are shown to be successful in modeling applications with
graph structures. However, training an accurate GNN model requires a large collection of …

Training deep graph neural networks (GNNs) is notoriously hard. Besides the standard
plights in training deep architectures such as vanishing gradients and overfitting, it also …

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 …

Recently, graph neural networks (GNNs) have revolutionized the field of graph
representation learning through effectively learned node embeddings, and achieved state-of …

Recently, Graph Neural Networks (GNNs) arouse a lot of research interest and achieve
great success in dealing with graph-based data. The basic idea of GNNs is to aggregate …

Graph convolutional networks (GCNs) are potentially short of the ability to learn hierarchical
representation for graph embedding, which holds them back in the graph classification task …