Higher-order relations are widespread in nature, with numerous phenomena involving
complex interactions that extend beyond simple pairwise connections. As a result …

We introduce Hyper-YOLO, a new object detection method that integrates hypergraph
computations to capture the complex high-order correlations among visual features …

Transcriptome‐wide association study (TWAS) methodologies aim to identify genetic effects
on phenotypes through the mediation of gene transcription. In TWAS, in silico models of …

Multicellular self-assembly into functional structures is a dynamic process that is critical in
the development of biological structures and diseases, including embryo development …

A primary challenge facing modern scientific research is the limited availability of gold-
standard data which can be both costly and labor-intensive to obtain. With the rapid …

Recently, hypergraph neural networks (HGNNs) exhibit the potential to tackle tasks with high-
order correlations and have achieved success in many tasks. However, existing evolution on …

Link prediction is a critical task within the realm of graph machine learning. While recent
advancements mainly emphasize node representation learning, the rich information …

Complex direct and indirect relationships between multiple variables, termed higher order
interactions (HOIs), are characteristics of all natural systems. Traditional differential and …

Practical real-world scenarios such as the Internet, social networks, and biological networks
present the challenges of data scarcity and complex correlations, which limit the applications …

Hypergraph Neural Networks (HGNNs) have recently attracted much attention and exhibited
satisfactory performance due to their superiority in high-order correlation modeling …