Feyer Stefan P, Pinaud Bruno, Kobourov Stephen, Brich Nicolas, Krone Michael, Kerren Andreas, Behrisch Michael, Schreiber Falk, Klein Karsten
IEEE Trans Vis Comput Graph. 2024 Jan;30(1):469-479. doi: 10.1109/TVCG.2023.3327402. Epub 2023 Dec 27.
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.
不同类型实体之间的关系信息通常由多层网络(MLN)建模——一种具有由层表示的子网的网络。在可视化表示中,MLN的层可以以不同方式排列,然而,这种排列对网络可读性的影响仍是一个悬而未决的问题。因此,我们针对与MLN分析相关的几个常见任务研究了这种影响。此外,在这种情况下常见的维度超过二维的层排列方式促使人们使用立体显示。我们进行了一项利用虚拟现实头戴式设备的人体研究,以评估二维、2.5维和三维层排列方式。该研究采用了六项分析任务,涵盖了MLN任务分类法的范围,从路径查找和模式识别到层间和跨层比较。我们没有发现明显的总体优胜者。然而,我们探索了任务与排列方式的空间,并得出了关于有效使用二维、2.5维和三维层排列方式用于MLN的基于经验的建议。