  In this work, we introduce a hypergraph representation learning framework
called Hypergraph Neural Networks (HNN) that jointly learns hyperedge
embeddings along with a set of hyperedge-dependent embeddings for each node in
the hypergraph. HNN derives multiple embeddings per node in the hypergraph
where each embedding for a node is dependent on a specific hyperedge of that
node. Notably, HNN is accurate, data-efficient, flexible with many
interchangeable components, and useful for a wide range of hypergraph learning
tasks. We evaluate the effectiveness of the HNN framework for hyperedge
prediction and hypergraph node classification. We find that HNN achieves an
overall mean gain of 7.72% and 11.37% across all baseline models and graphs for
hyperedge prediction and hypergraph node classification, respectively.
