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 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 …

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

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 …

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) have attracted an increasing attention in recent years.
However, most existing state-of-the-art graph learning methods only focus on node features …

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 …

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 neural networks (GNNs) have received tremendous attention due to their superiority
in learning node representations. These models rely on message passing and feature …

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 …