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 …

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 …

Graph Neural Networks (GNNs) have garnered considerable interest due to their
exceptional performance in a wide range of graph machine learning tasks. Nevertheless, the …

Next Point-of-Interest (POI) recommendation is a critical task in location-based services that
aim to provide personalized suggestions for the user's next destination. Previous works on …

This paper investigates traffic forecasting, which attempts to forecast the future state of traffic
based on historical situations. This problem has received ever-increasing attention in …

In the domain of graph-structured data learning, semi-supervised node classification serves
as a critical task, relying mainly on the information from unlabeled nodes and a minor …

Graph classification, aiming at learning the graph-level representations for effective class
assignments, has received outstanding achievements, which heavily relies on high-quality …

Despite the success of the carefully-annotated benchmarks, the effectiveness of existing
graph neural networks (GNNs) can be considerably impaired in practice when the real-world …

Label noise is a common challenge in large datasets, as it can significantly degrade the
generalization ability of deep neural networks. Most existing studies focus on noisy labels in …

Graph-level classification is a critical problem in social analysis and bioinformatics. Since
annotated labels are typically costly, we intend to study this challenging task in semi …