2D, 2.5D, or 3D? An Exploratory Study on Multilayer Network Visualisations in Virtual Reality

TL;DR

This study investigates how different 2D, 2.5D, and 3D visual arrangements of multilayer networks in virtual reality affect readability and task performance, providing empirical guidelines for effective visualization choices.

Contribution

It is the first comprehensive human study comparing 2D, 2.5D, and 3D multilayer network visualizations in VR across multiple analysis tasks.

Findings

No overall best arrangement identified.

Task-specific recommendations for visualization choices.

Empirical data on VR multilayer network readability.

Abstract

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.