  Hypergraph representations are both more efficient and better suited to
describe data characterized by relations between two or more objects. In this
work, we present a new graph neural network based on message passing capable of
processing hypergraph-structured data. We show that the proposed model defines
a design space for neural network models for hypergraphs, thus generalizing
existing models for hypergraphs. We report experiments on a benchmark dataset
for node classification, highlighting the effectiveness of the proposed model
with respect to other state-of-the-art methods for graphs and hypergraphs. We
also discuss the benefits of using hypergraph representations and, at the same
time, highlight the limitation of using equivalent graph representations when
the underlying problem has relations among more than two objects.
