It’s a Wrap: Toroidal Wrapping of Network Visualisations Supports Cluster Understanding Tasks. Chen, K., Dwyer, T., Bach, B., & Marriott, K. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, of CHI '21, New York, NY, USA, 2021. Association for Computing Machinery. ![It’s a Wrap: Toroidal Wrapping of Network Visualisations Supports Cluster Understanding Tasks [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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, finding 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 offer two algorithms for improving toroidal layout that is completely autonomous and automatic panning of the viewport to minimiswe wrapping links. The resulting layouts afford fewer crossings, less stress, and greater cluster separation. In a study of 32 participants comparing performance in cluster understanding tasks, we find that toroidal visualisation offers significant benefits over standard unwrapped visualisation in terms of improvement in error by 62.7% and time by 32.3%.
@inproceedings{10.1145/3411764.3445439,
author = {Chen, Kun-Ting and Dwyer, Tim and Bach, Benjamin and Marriott, Kim},
title = {It’s a Wrap: Toroidal Wrapping of Network Visualisations Supports Cluster Understanding Tasks},
year = {2021},
isbn = {9781450380966},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3411764.3445439},
doi = {10.1145/3411764.3445439},
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, finding 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 offer two algorithms for improving toroidal layout that is completely autonomous and automatic panning of the viewport to minimiswe wrapping links. The resulting layouts afford fewer crossings, less stress, and greater cluster separation. In a study of 32 participants comparing performance in cluster understanding tasks, we find that toroidal visualisation offers significant benefits over standard unwrapped visualisation in terms of improvement in error by 62.7% and time by 32.3%.},
booktitle = {Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems},
articleno = {465},
numpages = {12},
keywords = {User study., Torus topology, Network visualization, Layout algorithm, Graph visualization, Cluster visualisation},
location = {Yokohama, Japan},
series = {CHI '21}
}
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