Relations among multiple entities are prevalent in many fields, and hypergraphs are widely
used to represent such group relations. Hence, machine learning on hypergraphs has …

Hypergraphs are natural and expressive modeling tools to encode high-order relationships
among entities. Several variations of Hypergraph Neural Networks (HGNNs) are proposed …

Previous hypergraph expansions are solely carried out on either vertex level or hyperedge
level, thereby missing the symmetric nature of data co-occurrence, and resulting in …

Hypergraphs are generalizations of graphs where the (hyper) edges can connect any
number of vertices. They are powerful tools for representing complex and non-pairwise …

Hypergraph neural networks have recently drawn widespread attention and have
succeeded in many fields. However, existing hypergraph-based neural network approaches …

HyperGraph Convolutional Neural Networks (HGCNNs) have demonstrated their potential in
modeling high-order relations preserved in graph-structured data. However, most existing …

In recent years, hypergraph modeling has shown its superiority on correlation formulation
among samples and has wide applications in classification, retrieval, and other tasks. In all …

In recent years, hypergraph learning has attracted great attention due to its capacity in
representing complex and high-order relationships. However, current neural network …

Hypergraphs provide a natural representation for many real world datasets. We propose a
novel framework, HNHN, for hypergraph representation learning. HNHN is a hypergraph …

Hypergraph learning is a technique for conducting learning on a hypergraph structure. In
recent years, hypergraph learning has attracted increasing attention due to its flexibility and …