  Graphs (i.e., networks) have become an integral tool for the representation
and analysis of relational data. Advances in data gathering have lead to
multi-relational data sets which exhibit greater depth and scope. In certain
cases, this data can be modeled using a hypergraph. However, in practice
analysts typically reduce the dimensionality of the data (whether consciously
or otherwise) to accommodate a traditional graph model. In recent years
spectral hypergraph theory has emerged to study the eigenpairs of the adjacency
hypermatrix of a uniform hypergraph. We show how analyzing multi-relational
data, via a hypermatrix associated to the aforementioned hypergraph, can lead
to conclusions different from those when the data is projected down to its
co-occurrence matrix. In particular, we provide an example of a uniform
hypergraph where the most central vertex (\`a la eigencentrality) changes
depending on the order of the associated matrix. To the best of our knowledge
this is the first known hypergraph to exhibit this property.
