In clinical artificial intelligence (AI), graph representation learning, mainly through graph
neural networks and graph transformer architectures, stands out for its capability to capture …

Leading graph ordinary differential equation (ODE) models have offered generalized
strategies to model interacting multi-agent dynamical systems in a data-driven approach …

In the field of traffic forecasting, methods based on Graph Convolutional Network (GCN) are
emerging. But existing methods still have limitations due to insufficient sharing patterns …

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

Although graph neural networks (GNNs) have achieved impressive achievements in graph
classification, they often need abundant task-specific labels, which could be extensively …

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 …

Abstract Graph Neural Networks (GNNs) have achieved great success in representing data
with dependencies by recursively propagating and aggregating messages along the edges …

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

Graph classification is a crucial task in many real-world multimedia applications, where
graphs can represent various multimedia data types such as images, videos, and social …