NODKANT: Exploring Constructive Network Physicalization

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dc.contributor.authorPahr, Danielen_US
dc.contributor.authorBartolomeo, Sara Dien_US
dc.contributor.authorEhlers, Henryen_US
dc.contributor.authorFilipov, Velitchko Andreeven_US
dc.contributor.authorStoiber, Christinaen_US
dc.contributor.authorAigner, Wolfgangen_US
dc.contributor.authorWu, Hsiang-Yunen_US
dc.contributor.authorRaidou, Renata Georgiaen_US
dc.contributor.editorAigner, Wolfgangen_US
dc.contributor.editorAndrienko, Nataliaen_US
dc.contributor.editorWang, Beien_US
dc.date.accessioned2025-05-26T06:39:01Z
dc.date.available2025-05-26T06:39:01Z
dc.date.issued2025
dc.description.abstractPhysicalizations, 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/.en_US
dc.description.sectionheadersBest Papers
dc.description.seriesinformationComputer Graphics Forum
dc.identifier.doi10.1111/cgf.70140
dc.identifier.issn1467-8659
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.70140
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf70140
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing → Visualization application domains; Empirical studies in visualization
dc.subjectHuman centered computing → Visualization application domains
dc.subjectEmpirical studies in visualization
dc.titleNODKANT: Exploring Constructive Network Physicalizationen_US
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