Esteban Bautista, Matthieu Latapy
In 10th International Conference on Complex Networks and their Applications, Madrid (Spain), December 2021 (Poster)
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 pat-
terns across all dimensions and to identify the best definitions for a given dataset. This
work overrides this vision and proposes a direct processing of the set of triplets. While
 also approaches triplets directly, it focuses on specific patterns and applications.
Our work shows that a more general analysis is possible by partitioning the data and
building categorical propositions (CPs) 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 CPs satisfying specific constraints and apply it to a real world dataset.