Egocentric Network Exploration for Immersive Analytics

Information

• Publication Type: Journal Paper with Conference Talk
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: October 2021
• Journal: Computer Graphics Forum
• Volume: 40
• Open Access: no
• Location: Wellington, NZ
• Lecturer: Johannes Sorger
• Event: Pacific Graphics 21
• DOI: 10.1111/cgf.14417
• Call for Papers: Call for Paper
• Pages: 12
• Publisher: John Wiley and Sons
• Conference date: 18. October 2021 – 21. October 2021
• Pages: 241 – 252
• Keywords: Computer Graphics and Computer-Aided Design

Abstract

To exploit the potential of immersive network analytics for engaging and effective exploration, we promote the metaphor of ``egocentrism'', where data depiction and interaction are adapted to the perspective of the user within a 3D network. Egocentrism has the potential to overcome some of the inherent downsides of virtual environments, e.g., visual clutter and cyber-sickness. To investigate the effect of this metaphor on immersive network exploration, we designed and evaluated interfaces of varying degrees of egocentrism. In a user study, we evaluated the effect of these interfaces on visual search tasks, efficiency of network traversal, spatial orientation, as well as cyber-sickness. Results show that a simple egocentric interface considerably improves visual search efficiency and navigation performance, yet does not decrease spatial orientation or increase cyber-sickness. A distorted occlusion-free view of the neighborhood only marginally improves the user's performance. We tie our findings together in an open online tool for egocentric network exploration, providing actionable insights on the benefits of the egocentric network exploration metaphor.

Additional Files and Images

Weblinks

• online egocentric network
  Online tool for egocentric network exploration leveraging insights gathered at the user study
• Entry in reposiTUm (TU Wien Publication Database)
• Entry in the publication database of TU-Wien
• DOI: 10.1111/cgf.14417

BibTeX

@article{sorger-2021-egonet,
  title =      "Egocentric Network Exploration for Immersive Analytics",
  author =     "Johannes Sorger and Alessio Arleo and Peter K\'{a}n and
               Wolfgang Knecht and Manuela Waldner",
  year =       "2021",
  abstract =   "To exploit the potential of immersive network analytics for
               engaging and effective exploration, we promote the metaphor
               of ``egocentrism'', where data depiction and interaction are
               adapted to the perspective of the user within a 3D network.
               Egocentrism has the potential to overcome some of the
               inherent downsides of virtual environments, e.g., visual
               clutter and cyber-sickness. To investigate the effect of
               this metaphor on immersive network exploration, we designed
               and evaluated interfaces of varying degrees of egocentrism.
               In a user study, we evaluated the effect of these interfaces
               on visual search tasks, efficiency of network traversal,
               spatial orientation, as well as cyber-sickness. Results show
               that a simple egocentric interface considerably improves
               visual search efficiency and navigation performance, yet
               does not decrease spatial orientation or increase
               cyber-sickness. A distorted occlusion-free view of the
               neighborhood only marginally improves the user's
               performance. We tie our findings together in an open online
               tool for egocentric network exploration, providing
               actionable insights on the benefits of the egocentric
               network exploration metaphor.",
  month =      oct,
  journal =    "Computer Graphics Forum",
  volume =     "40",
  doi =        "10.1111/cgf.14417",
  pages =      "12",
  publisher =  "John Wiley and Sons",
  pages =      "241--252",
  keywords =   "Computer Graphics and Computer-Aided Design",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2021/sorger-2021-egonet/",
}