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 …

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 …

Graphs are widely used to describe real-world objects and their interactions. Graph Neural
Networks (GNNs) as a de facto model for analyzing graphstructured data, are highly …

Abstract Graph Neural Networks (GNNs) have emerged as the de facto standard for
representation learning on graphs, owing to their ability to effectively integrate graph …

Abstract Graph Structure Learning (GSL) has recently garnered considerable attention due
to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the …

Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural
networks (GNNs), has shown great potential in boosting the performance of GNNs. Most …

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\textbf {I}
terative\textbf {D} eep\textbf {G} raph\textbf {L} earning (\alg), for jointly and iteratively …

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 …

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 …