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 …

The recently developed unsupervised graph representation learning approaches apply
contrastive learning into graph-structured data and achieve promising performance …

This paper studies few-shot molecular property prediction, which is a fundamental problem
in cheminformatics and drug discovery. More recently, graph neural network based model …

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

This paper studies the problem of traffic flow forecasting, which aims to predict future traffic
conditions on the basis of road networks and traffic conditions in the past. The problem is …

Sequential recommendation aims at understanding user preference by capturing successive
behavior correlations, which are usually represented as the item purchasing sequences …

Graph clustering, which learns the node representations for effective cluster assignments, is
a fundamental yet challenging task in data analysis and has received considerable attention …

This paper studies the problem of graph-level clustering, which is a novel yet challenging
task. This problem is critical in a variety of real-world applications such as protein clustering …

Network biology is an interdisciplinary field bridging computational and biological sciences
that has proved pivotal in advancing the understanding of cellular functions and diseases …

In recent years, deep learning on graphs has achieved remarkable success in various
domains. However, the reliance on annotated graph data remains a significant bottleneck …