NODKANT: Exploring Constructive Network Physicalization

Physicalizations, which combine perceptual and sensorimotor interactions, offer an immersive way to comprehend complex datavisualizations by stimulating active construction and manipulation. This study investigates the impact of personal constructionon the comprehension of physicalized networks. We propose a physicalization toolkit—NODKANT—for constructing modularnode-link diagrams consisting of a magnetic surface, 3D printable and stackable node labels, and edges of adjustable length. Ina mixed-methods between-subject lab study with 27 participants, three groups of people used NODKANT to complete a seriesof low-level analysis tasks in the context of an animal contact network. The first group was tasked with freely constructingtheir network using a sorted edge list, the second group received step-by-step instructions to create a predefined layout, andthe third group received a pre-constructed representation. While free construction proved on average more time-consuming, weshow 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 inusers’ subjective task load. Finally, our findings indicate that participants who constructed their own representations were ableto recall more detailed insights after a period of 10–14 days compared to those who were given a pre-constructed networkphysicalization. All materials, data, code for generating instructions, and 3D printable meshes are available on https://osf.io/tk3g5/?view_only=e9e862ef2ca442488cb7a684ac841f03.