Wiggle! Wiggle! Wiggle! Visualizing uncertainty in node attributes in straight-line node-link diagrams using animated wiggliness

Information

• Publication Type: Journal Paper (without talk)
• Workgroup(s)/Project(s): not specified
• Date: October 2025
• Article Number: 104290
• DOI: 10.1016/j.cag.2025.104290
• ISSN: 1873-7684
• Journal: COMPUTERS & GRAPHICS-UK
• Volume: 131
• Publisher: PERGAMON-ELSEVIER SCIENCE LTD
• Keywords: Network Visualization, Uncertainty Visualization, Animation, Fuzziness, Enclosure, Saturation

Abstract

Uncertainty is common to most types of data, from meteorology to the biomedical sciences. Here, we are interested in the visualization of uncertainty within the context of multivariate graphs, specifically the visualization of uncertainty attached to node attributes. Many visual channels offer themselves up for the visualization of node attributes and their uncertainty. One controversial and relatively under-explored channel, however, is animation, despite its conceptual advantages. In this paper, we investigate node “wiggliness”, i.e. uncertainty-dependent pseudo-random motion of nodes, as a potential new visual channel with which to communicate node attribute uncertainty. To study wiggliness’ effectiveness, we compare it against three other visual channels identified from a thorough review of uncertainty visualization literature—namely node enclosure, node fuzziness, and node color saturation. In a larger-scale, mixed method, Prolific-crowd-sourced, online user study of 160 participants, we quantitatively and qualitatively compare these four uncertainty encodings across eight low-level graph analysis tasks that probe participants’ abilities to parse the presented networks both on an attribute and topological level. We ultimately conclude that all four uncertainty encodings appear comparably useful—as opposed to previous findings. Wiggliness may be a suitable and effective visual channel with which to communicate node attribute uncertainty, at least for the kinds of data and tasks considered in our study.

Additional Files and Images

Weblinks

• Entry in reposiTUm (TU Wien Publication Database)
• DOI: 10.1016/j.cag.2025.104290

BibTeX

@article{ehlers-2025-www,
  title =      "Wiggle! Wiggle! Wiggle! Visualizing uncertainty in node
               attributes in straight-line node-link diagrams using
               animated wiggliness",
  author =     "Henry Ehlers and Daniel Pahr and Sara Di Bartolomeo and
               Velitchko Filipov and Hsiang-Yun Wu and Renata Raidou",
  year =       "2025",
  abstract =   "Uncertainty is common to most types of data, from
               meteorology to the biomedical sciences. Here, we are
               interested in the visualization of uncertainty within the
               context of multivariate graphs, specifically the
               visualization of uncertainty attached to node attributes.
               Many visual channels offer themselves up for the
               visualization of node attributes and their uncertainty. One
               controversial and relatively under-explored channel,
               however, is animation, despite its conceptual advantages. In
               this paper, we investigate node “wiggliness”, i.e.
               uncertainty-dependent pseudo-random motion of nodes, as a
               potential new visual channel with which to communicate node
               attribute uncertainty. To study wiggliness’ effectiveness,
               we compare it against three other visual channels identified
               from a thorough review of uncertainty visualization
               literature—namely node enclosure, node fuzziness, and node
               color saturation. In a larger-scale, mixed method,
               Prolific-crowd-sourced, online user study of 160
               participants, we quantitatively and qualitatively compare
               these four uncertainty encodings across eight low-level
               graph analysis tasks that probe participants’ abilities to
               parse the presented networks both on an attribute and
               topological level. We ultimately conclude that all four
               uncertainty encodings appear comparably useful—as opposed
               to previous findings. Wiggliness may be a suitable and
               effective visual channel with which to communicate node
               attribute uncertainty, at least for the kinds of data and
               tasks considered in our study.",
  month =      oct,
  articleno =  "104290",
  doi =        "10.1016/j.cag.2025.104290",
  issn =       "1873-7684",
  journal =    "COMPUTERS & GRAPHICS-UK",
  volume =     "131",
  publisher =  "PERGAMON-ELSEVIER SCIENCE LTD",
  keywords =   "Network Visualization, Uncertainty Visualization, Animation,
               Fuzziness, Enclosure, Saturation",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2025/ehlers-2025-www/",
}