Biological entities are involved in intricate and complex interactions, in which uncovering the
biological information from the network concepts are of great significance. Benefiting from …

This paper studies unsupervised/self-supervised whole-graph representation learning,
which is critical in many tasks such as molecule properties prediction in drug and material …

New drug discovery is inseparable from the discovery of drug targets, and the vast majority
of the known targets are proteins. At the same time, proteins are essential structural and …

Unsupervised graph-level representation learning has recently shown great potential in a
variety of domains, ranging from bioinformatics to social networks. Plenty of graph …

Graph-level representations are critical in various real-world applications, such as predicting
the properties of molecules. However, in practice, precise graph annotations are generally …

Graph-level representation learning is to learn lowdimensional representation for the entire
graph, which has shown a large impact on real-world applications. Recently, limited by …

Graph classification is a fundamental problem with diverse applications in bioinformatics
and chemistry. Due to the intricate procedures of manual annotations in graphical domains …

The success of graph neural networks (GNNs) largely relies on the process of aggregating
information from neighbors defined by the input graph structures. Notably, message passing …

This article studies self-supervised graph representation learning, which is critical to various
tasks, such as protein property prediction. Existing methods typically aggregate …

Self-supervised learning for graph neural networks has attracted considerable attention and
shows notable successes in graph representation learning. However, the formation of a real …