There has been a lot of activity in graph representation learning in recent years. Graph
representation learning aims to produce graph representation vectors to represent the …

Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

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

Graphs are ubiquitous and play an essential role in the real world. A vital prerequisite for
analyzing graphs is to learn their effective representations. Most existing graph …

Graphs naturally appear in numerous application domains, ranging from social analysis,
bioinformatics to computer vision. The unique capability of graphs enables capturing the …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Graph representation learning is of paramount importance for a variety of graph analytical
tasks, ranging from node classification to community detection. Recently, graph …

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 academia and industries, graph neural networks (GNNs) have emerged as a powerful
approach to graph data processing ranging from node classification and link prediction tasks …