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联关系常通过多层网络（MLN）建模，该网络由各层表示的多个子网络构成。在可视化表示中，MLN的层级可采用不同方式进行排列，然而这种排列方式对网络可读性的影响仍是待解问题。为此，我们针对MLN分析中若干常见任务研究了该影响。此外，该场景下常见的高于二维的层级排列方式，促使我们考虑使用立体显示设备。我们开展了一项利用虚拟现实头盔的人类受试者实验，评估了二维、二点五维和三维层级排列的效果。研究采用六项覆盖MLN任务分类谱系的分析任务，涵盖路径查找、模式识别及跨层/层间比较等类型。实验未发现明确的全局最优方案，但我们通过探索任务与排列的对应空间，针对MLN中二维、二点五维和三维层级排列的有效使用提出了基于实证的推荐建议。