Graph is a natural data structure for describing complex systems, which contains a set of
objects and relationships. Ubiquitous real-life biomedical problems can be modeled as …

Graph is an important data representation which appears in a wide diversity of real-world
scenarios. Effective graph analytics provides users a deeper understanding of what is …

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 …

Generalizable, transferrable, and robust representation learning on graph-structured data
remains a challenge for current graph neural networks (GNNs). Unlike what has been …

Graphs are widely used as a popular representation of the network structure of connected
data. Graph data can be found in a broad spectrum of application domains such as social …

Deep supervised learning has achieved great success in the last decade. However, its
defects of heavy dependence on manual labels and vulnerability to attacks have driven …

We introduce a self-supervised approach for learning node and graph level representations
by contrasting structural views of graphs. We show that unlike visual representation learning …

Graph representation learning has emerged as a powerful technique for addressing real-
world problems. Various downstream graph learning tasks have benefited from its recent …

Graph representation learning nowadays becomes fundamental in analyzing graph-
structured data. Inspired by recent success of contrastive methods, in this paper, we propose …

Message-passing neural networks (MPNNs) have been successfully applied to
representation learning on graphs in a variety of real-world applications. However, two …