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 [1]. 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

[2] 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.