  The theory of Group Equivariant Non-Expansive Operators (GENEOs) was
initially developed in Topological Data Analysis for the geometric
approximation of data observers, including their invariances and symmetries.
This paper departs from that line of research and explores the use of GENEOs
for distinguishing $r$-regular graphs up to isomorphisms. In doing so, we aim
to test the capabilities and flexibility of these operators. Our experiments
show that GENEOs offer a good compromise between efficiency and computational
cost in comparing $r$-regular graphs, while their actions on data are easily
interpretable. This supports the idea that GENEOs could be a general-purpose
approach to discriminative problems in Machine Learning when some structural
information about data and observers is explicitly given.
