  For more than a decade, graphs have been used to model the voting behavior
taking place in parliaments. However, the methods described in the literature
suffer from several limitations. The two main ones are that 1) they rely on
some temporal integration of the raw data, which causes some information loss,
and/or 2) they identify groups of antagonistic voters, but not the context
associated to their occurrence. In this article, we propose a novel method
taking advantage of multiplex signed graphs to solve both these issues. It
consists in first partitioning separately each layer, before grouping these
partitions by similarity. We show the interest of our approach by applying it
to a European Parliament dataset.
