  Recently, neural network architectures have been developed to accommodate
when the data has the structure of a graph or, more generally, a hypergraph.
While useful, graph structures can be potentially limiting. Hypergraph
structures in general do not account for higher order relations between their
hyperedges. Simplicial complexes offer a middle ground, with a rich theory to
draw on. We develop a convolutional neural network layer on simplicial
2-complexes.
