Hypergraph Model

Hypergraph models represent data by considering higher-order relationships between multiple data points simultaneously, unlike traditional graphs which only model pairwise connections. Current research focuses on developing efficient algorithms for hypergraph construction and analysis, including spectral methods, hypergraph neural networks (HGNNs), and transformer-based architectures, often tailored to specific applications like imbalanced data classification or time series forecasting. These advancements are improving performance in various fields, such as disease subtyping using electronic health records, action recognition from event cameras, and autism spectrum disorder classification from brain imaging data, by leveraging the richer information captured by hypergraphs. The ability to model complex, multi-way interactions makes hypergraphs a powerful tool for data analysis across diverse scientific domains.