DoughNets: Visualising Networks Using Torus Wrapping

Kun-Ting Chen, Tim Dwyer, Kim Marriott, Benjamin Bach

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

We investigate visualisations of networks on a 2-dimensional torus topology, like an opened-up and flattened doughnut. That is, the network is drawn on a rectangular area while “wrapping” specific links around the border. Previous work on torus drawings of networks has been mostly theoretical, limited to certain classes of networks, and not evaluated by human readability studies. We offer a simple interactive layout approach applicable to general graphs. We use this to find layouts affording better aesthetics in terms of conventional measures like more equal edge length and fewer crossings. In two controlled user studies we find that torus layout with either additional context or interactive panning provided significant performance improvement (in terms of error and time) over torus layout without either of these improvements, to the point that it is comparable to standard non-torus layout.
Original languageEnglish
Title of host publicationCHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
Number of pages11
ISBN (Print)9781450367080
Publication statusPublished - 21 Apr 2020
EventACM CHI Conference on Human Factors in Computing Systems - Hawaiʻi Convention Center on the island of Oahu, Honolulu, United States
Duration: 25 Apr 202030 Apr 2020


ConferenceACM CHI Conference on Human Factors in Computing Systems
Abbreviated titleCHI 2020
Country/TerritoryUnited States
Internet address

Keywords / Materials (for Non-textual outputs)

  • Graph Visualization
  • Network Visualization
  • Torus Topology
  • User Study


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