Graph-based learning techniques traditionally focus on pairwise relationships, modelling them as edges between two nodes. Hypergraphs generalise this concept by allowing edges—known as hyperedges—to ...

One of the most compelling—and still underexplored—capabilities of Hypergraph Neural Networks lies in their ability to learn heuristics. In this context, heuristics are not hand-crafted rules, but ...

Abstract: Multimodal learning has garnered significant attention in the medical field due to its ability to provide a more comprehensive perspective utilizing various types of data, that aids in ...

Hypergraphs generalize graphs by allowing edges that connect multiple nodes simultaneously. They are powerful tools for modeling higher-order relationships in data and are increasingly important in ...

Hypergraphs, with their ability to model complex, beyond pair-wise correlations, presents a significant advancement over traditional graphs for capturing intricate relational data across diverse ...

🎯 Goal: Existing methods normally exploit the pair-wise relation among target samples and attempt to discover their correlations by clustering these samples based on semantic features. The drawbacks ...