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 …

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

Histopathology, which is the gold-standard for prostate cancer diagnosis, faces significant
challenges. With prostate cancer ranking among the most common cancers in the United …

The propagation of rumours on social media poses an important threat to societies, so that
various techniques for graph-based rumour detection have been proposed recently. Existing …

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 …

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 …

Abstract Graph Neural Networks (GNNs) have emerged as the most powerful weapon for
various graph tasks due to the message-passing mechanism's great local information …

Understanding drug–drug interactions (DDI) of new drugs is critical for minimizing
unexpected adverse drug reactions. The modeling of new drugs is called a cold start …