NODKANT: exploring constructive network physicalization

Information

• Publication Type: Journal Paper with Conference Talk
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: 2025
• Journal: Computer Graphics Forum
• Open Access: yes
• Article Number: e70140
• ISSN: 1467-8659
• DOI: 10.1111/cgf.70140
• Pages: 12
• Publisher: WILEY
• Keywords: CCS Concepts, Empirical studies in visualization, human-centered computing, Visualization Application Domains

Abstract

Physicalizations, which combine perceptual and sensorimotor interactions, offer an immersive way to comprehend complex data visualizations by stimulating active construction and manipulation. This study investigates the impact of personal construction on the comprehension of physicalized networks. We propose a physicalization toolkit—NODKANT—for constructing modular node-link diagrams consisting of a magnetic surface, 3D printable and stackable node labels, and edges of adjustable length. In a mixed-methods between-subject lab study with 27 participants, three groups of people used NODKANT to complete a series of low-level analysis tasks in the context of an animal contact network. The first group was tasked with freely constructing their network using a sorted edge list, the second group received step-by-step instructions to create a predefined layout, and the third group received a pre-constructed representation. While free construction proved on average more time-consuming, we show that users extract more insights from the data during construction and interact with their representation more frequently, compared to those presented with step-by-step instructions. Interestingly, the increased time demand cannot be measured in users' subjective task load. Finally, our findings indicate that participants who constructed their own representations were able to recall more detailed insights after a period of 10–14 days compared to those who were given a pre-constructed network physicalization. All materials, data, code for generating instructions, and 3D printable meshes are available on https://osf.io/tk3g5/.

Additional Files and Images

Weblinks

• Entry in reposiTUm (TU Wien Publication Database)
• CatalogPlus (TU Wien Library)
• DOI: 10.1111/cgf.70140

BibTeX

@article{pahr-2025-nodkant,
  title =      "NODKANT: exploring constructive network physicalization",
  author =     "Daniel Pahr and S. Di Bartolomeo and Henry Ehlers and
               Velitchko Filipov and C. Stoiber and W Aigner and Hsiang-Yun
               Wu and Renata Raidou",
  year =       "2025",
  abstract =   "Physicalizations, which combine perceptual and sensorimotor
               interactions, offer an immersive way to comprehend complex
               data visualizations by stimulating active construction and
               manipulation. This study investigates the impact of personal
               construction on the comprehension of physicalized networks.
               We propose a physicalization toolkit—NODKANT—for
               constructing modular node-link diagrams consisting of a
               magnetic surface, 3D printable and stackable node labels,
               and edges of adjustable length. In a mixed-methods
               between-subject lab study with 27 participants, three groups
               of people used NODKANT to complete a series of low-level
               analysis tasks in the context of an animal contact network.
               The first group was tasked with freely constructing their
               network using a sorted edge list, the second group received
               step-by-step instructions to create a predefined layout, and
               the third group received a pre-constructed representation.
               While free construction proved on average more
               time-consuming, we show that users extract more insights
               from the data during construction and interact with their
               representation more frequently, compared to those presented
               with step-by-step instructions. Interestingly, the increased
               time demand cannot be measured in users' subjective task
               load. Finally, our findings indicate that participants who
               constructed their own representations were able to recall
               more detailed insights after a period of 10–14 days
               compared to those who were given a pre-constructed network
               physicalization. All materials, data, code for generating
               instructions, and 3D printable meshes are available on
               https://osf.io/tk3g5/.",
  journal =    "Computer Graphics Forum",
  articleno =  "e70140",
  issn =       "1467-8659",
  doi =        "10.1111/cgf.70140",
  pages =      "12",
  publisher =  "WILEY",
  keywords =   "CCS Concepts, Empirical studies in visualization,
               human-centered computing, Visualization Application Domains",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2025/pahr-2025-nodkant/",
}