Hypergraph Datasets

Hypergraph datasets, representing complex relationships beyond pairwise interactions, are increasingly studied to model higher-order correlations in data. Current research focuses on developing efficient and expressive hypergraph neural networks (HGNNs), exploring architectures like message-passing and tensor-based methods, and addressing computational challenges through techniques such as knowledge distillation into simpler models. These advancements aim to improve the accuracy and scalability of hypergraph analysis for applications ranging from link prediction and node classification to real-world tasks like return-to-origin prediction in e-commerce. The development of robust and efficient HGNNs is crucial for unlocking the potential of hypergraph data in various scientific domains and practical applications.