%0 Conference Paper %1 Chen2021 %A Chen, Kun-Ting %A Dwyer, Tim %A Bach, Benjamin %A Marriott, Kim %B Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems %C New York, NY, USA %D 2021 %I Association for Computing Machinery %K 2021 visus visus:chenkg %P 1–12 %R 10.1145/3411764.3445439 %T It’s a Wrap: Toroidal Wrapping of Network Visualisations Supports Cluster Understanding Tasks %U https://doi.org/10.1145/3411764.3445439 %X 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%. %@ 9781450380966

@inproceedings{Chen2021, 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%.}, added-at = {2022-09-28T17:34:43.000+0200}, address = {New York, NY, USA}, author = {Chen, Kun-Ting and Dwyer, Tim and Bach, Benjamin and Marriott, Kim}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2e8b681a6204838f9fb0786a2fa9da13b/pambrisi}, booktitle = {Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems}, day = 6, doi = {10.1145/3411764.3445439}, interhash = {6e8a97c6e366a6fda11fc0d396eebc3a}, intrahash = {e8b681a6204838f9fb0786a2fa9da13b}, isbn = {9781450380966}, keywords = {2021 visus visus:chenkg}, location = {Yokohama, Japan}, month = {5}, pages = {1–12}, publisher = {Association for Computing Machinery}, series = {CHI '21}, timestamp = {2022-09-28T15:34:43.000+0200}, title = {It’s a Wrap: Toroidal Wrapping of Network Visualisations Supports Cluster Understanding Tasks}, url = {https://doi.org/10.1145/3411764.3445439}, year = 2021 }