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 …

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 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 …

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 …

Point-of-Interest (POI) recommendation plays a vital role in various location-aware services.
It has been observed that POI recommendation is driven by both sequential and …

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 …

Recently, graph neural networks (GNNs) have shown prominent performance in semi-
supervised node classification by leveraging knowledge from the graph database. However …

In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive
self-supervised learning to model large-scale heterogeneous data. Many existing foundation …