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 neural networks (GNNs) have emerged as a powerful tool for effectively mining and
learning from graph-structured data, with applications spanning numerous domains …

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 …

Tensor Attention, a multi-view attention that is able to capture high-order correlations among
multiple modalities, can overcome the representational limitations of classical matrix …

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

Continuous graph neural networks (CGNNs) have garnered significant attention due to their
ability to generalize existing discrete graph neural networks (GNNs) by introducing …

Learning multi-agent system dynamics have been extensively studied for various real-world
applications, such as molecular dynamics in biology, multi-body system prediction in …

Fine-grained traffic forecasting is crucial for the management of urban transportation
systems. Road segments and intersection turns, as vital elements of road networks, exhibit …

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

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