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 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 …

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

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

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

Ultrasound is a critical imaging technique for diagnosing breast cancer. However, the
multimodal breast ultrasound diagnostic process is time-consuming and labor-intensive …

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 …