It's awrap: Toroidalwrapping of network visualisations supports cluster understanding tasks

Abstract

We explore network visualisation on a two-dimensional torus topology that continuously wraps when the viewport is panned. That is, links may be wrapped across the boundary, allowing additional spreading of node positions to reduce visual clutter. Recent work has investigated such pannable wrapped visualisations, fnding them not worse than unwrapped drawings for small networks for path-following tasks. However, they did not evaluate larger networks nor did they consider whether torus-based layout might also better display high-level network structure like clusters. We ofer two algorithms for improving toroidal layout that is completely autonomous and automatic panning of the viewport to minimiswe wrapping links. The resulting layouts aford fewer crossings, less stress, and greater cluster separation. In a study of 32 participants comparing performance in cluster understanding tasks, we fnd that toroidal visualisation ofers signifcant benefts over standard unwrapped visualisation in terms of improvement in error by 62.7% and time by 32.3%.