  Many systems generate data as a set of triplets (a, b, c): they may represent
that user a called b at time c or that customer a purchased product b in store
c. These datasets are traditionally studied as networks with an extra dimension
(time or layer), for which the fields of temporal and multiplex networks have
extended graph theory to account for the new dimension. However, such
frameworks detach one variable from the others and allow to extend one same
concept in many ways, making it hard to capture patterns across all dimensions
and to identify the best definitions for a given dataset. This extended
abstract overrides this vision and proposes a direct processing of the set of
triplets. In particular, our work shows that a more general analysis is
possible by partitioning the data and building categorical propositions that
encode informative patterns. We show that several concepts from graph theory
can be framed under this formalism and leverage such insights to extend the
concepts to data triplets. Lastly, we propose an algorithm to list propositions
satisfying specific constraints and apply it to a real world dataset.
