  Elements composing complex systems usually interact in several different ways
and as such the interaction architecture is well modelled by a multiplex
network. However often this architecture is hidden, as one usually only has
experimental access to an aggregated projection. A fundamental challenge is
thus to determine whether the hidden underlying architecture of complex systems
is better modelled as a single interaction layer or results from the
aggregation and interplay of multiple layers. Here we show that using local
information provided by a random walker navigating the aggregated network one
can decide in a robust way if the underlying structure is a multiplex or not
and, in the former case, to determine the most probable number of hidden
layers. As a byproduct, we show that the mathematical formalism also provides a
principled solution for the optimal decomposition and projection of complex,
non-Markovian dynamics into a Markov switching combination of diffusive modes.
We validate the proposed methodology with numerical simulations of both (i)
random walks navigating hidden multiplex networks (thereby reconstructing the
true hidden architecture) and (ii) Markovian and non-Markovian continuous
stochastic processes (thereby reconstructing an effective multiplex
decomposition where each layer accounts for a different diffusive mode). We
also state and prove two existence theorems guaranteeing that an exact
reconstruction of the dynamics in terms of these hidden jump-Markov models is
always possible for arbitrary finite-order Markovian and fully non-Markovian
processes. Finally, we showcase the applicability of the method to experimental
recordings from (i) the mobility dynamics of human players in an online
multiplayer game and (ii) the dynamics of RNA polymerases at the
single-molecule level.
