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.

翻译：不同类型实体之间的关系通常由多层网络（MLN）建模——一种以层表示子网的网络结构。在视觉表示中，多层网络的各层可采用不同方式排列，然而排列方式对网络可读性的影响仍是一个悬而未决的问题。为此，我们针对MLN分析中若干常见任务研究了这种影响。此外，超出二维维度的层排列方式（在此场景中十分普遍）促使我们采用立体显示技术。我们利用虚拟现实头戴设备开展了人因实验，评估了2D、2.5D和3D层排列方式。实验涵盖六项分析任务，覆盖MLN任务分类谱系——从路径查找、模式识别到层间及跨层比较。研究发现未出现明确的全局最优方案，但通过探索任务-排列空间，我们得出了关于MLN中2D、2.5D与3D层排列有效使用的经验性建议。