  This article presents a theoretical investigation of generalized encoded
forms of networks in a uniform multidimensional space. First, we study encoded
networks with (finite) arbitrary node dimensions (or aspects), such as time
instants or layers. In particular, we study these networks that are formalized
in the form of multiaspect graphs. In the context of node-aligned non-uniform
(or node-unaligned non-uniform and uniform) multidimensional spaces, previous
results has shown that, unlike classical graphs, the algorithmic information of
a multidimensional network is not in general dominated by the algorithmic
information of the binary sequence that determines the presence or absence of
edges. In the present work, first we demonstrate the existence of such
algorithmic information distortions for node-aligned uniform multidimensional
networks. Secondly, we show that there are particular cases of infinite nesting
families of finite uniform multidimensional networks such that each member of
these families is incompressible. From these results, we also recover the
network topological properties and equivalences in irreducible information
content of multidimensional networks in comparison to their isomorphic
classical graph counterpart in the previous literature. These results together
establish a universal algorithmic approach and set limitations and conditions
for irreducible information content analysis in comparing arbitrary networks
with a large number of dimensions, such as multilayer networks.
