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 …

Fatigue life prediction is critical for ensuring the safe service and the structural integrity of
mechanical structures. Although data-driven approaches have been proven effective in …

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 semi-supervised graph classification, a crucial task with a wide range of
applications in social network analysis and bioinformatics. Recent works typically adopt …

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 …

Graph classification is a critical task in numerous multimedia applications, where graphs are
employed to represent diverse types of multimedia data, including images, videos, and …

Node classification on graphs is a significant task with a wide range of applications,
including social analysis and anomaly detection. Even though graph neural networks …

Although graph representation learning has been studied extensively in static graph
settings, dynamic graphs are less investigated in this context. This paper proposes a novel …

Graph contrastive learning (GCL), aiming to generate supervision information by
transforming the graph data itself, is increasingly becoming a focus of graph research. It has …