Hypergraph Learning

Hypergraph learning focuses on analyzing data with higher-order relationships—interactions involving more than two entities—which are common in many real-world scenarios. Current research emphasizes developing novel model architectures, such as hypergraph neural networks (HGNNs) and hypergraph transformers, to effectively capture these complex relationships, often incorporating techniques like self-supervised learning and multi-view learning to address challenges such as imbalanced data and missing information. These advancements are proving valuable in diverse applications, including recommender systems, traffic flow prediction, medical diagnosis, and anomaly detection, by enabling more accurate and insightful analysis of intricate data structures.