  Relational information between different types of entities is often modelled
by a multilayer network (MLN) -- a network with subnetworks represented by
layers. The layers of an MLN can be arranged in different ways in a visual
representation, however, the impact of the arrangement on the readability of
the network is an open question. Therefore, we studied this impact for several
commonly occurring tasks related to MLN analysis. Additionally, layer
arrangements with a dimensionality beyond 2D, which are common in this
scenario, motivate the use of stereoscopic displays. We ran a human subject
study utilising a Virtual Reality headset to evaluate 2D, 2.5D, and 3D layer
arrangements. The study employs six analysis tasks that cover the spectrum of
an MLN task taxonomy, from path finding and pattern identification to
comparisons between and across layers. We found no clear overall winner.
However, we explore the task-to-arrangement space and derive empirical-based
recommendations on the effective use of 2D, 2.5D, and 3D layer arrangements for
MLNs.
