Hypergraphs, which belong to the family of higher-order networks, are a natural and
powerful choice for modeling group interactions in the real world. For example, when …

Abstract Graph autoencoders (Graph-AEs) learn representations of given graphs by aiming
to accurately reconstruct them. A notable application of Graph-AEs is graph-level anomaly …

In recent years, Graph Neural Networks (GNNs) have made significant advances in
processing structured data. However, most of them primarily adopted a model-centric …

Hypergraphs are widely employed to represent complex higher-order relations in real-world
applications. Most hypergraph learning research focuses on node-level or edge-level tasks …

Deep graph learning and network science both analyze graphs but approach similar
problems from different perspectives. Whereas network science focuses on models and …

Higher-order information is crucial for relational learning in many domains where
relationships extend beyond pairwise interactions. Hypergraphs provide a natural …

Most of the existing hypernetwork representation learning methods fail to fully consider the
hyperedges, leading to the untapped potential of information contained within the …

The conventional definition of hypergraph has two major issues:(1) there is not a standard
definition of directed hypergraph and (2) there is not a formal definition of nested …

Skeleton-based action recognition has gained significant attention for its ability to efficiently
represent spatiotemporal information in a lightweight format. Most existing approaches use …

Hypergraphs are crucial for modelling higher-order interactions in real-world data.
Hypergraph neural networks (HNNs) effectively utilise these structures by message passing …