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Towards a Better Understanding of Graph Perception in Immersive Environments

Authors Lin Zhang , Yao Wang , Ying Zhang , Wilhelm Kerle-Malcharek , Karsten Klein , Falk Schreiber , Andreas Bulling

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Subject Classification

ACM Subject Classification
  • Human-centered computing → Empirical studies in visualization
  • Human-centered computing → Virtual reality
  • Human-centered computing → Visual analytics
Keywords
  • Stereoscopic 3D
  • Graph Visualisation
  • Eye Tracking
  • Graph Perception

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Abstract

As Immersive Analytics (IA) increasingly uses Virtual Reality (VR) for stereoscopic 3D (S3D) graph visualisation, it is crucial to understand how users perceive network structures in these immersive environments. However, little is known about how humans read S3D graphs during task solving, and how gaze behaviour indicates task performance. To address this gap, we report a user study with 18 participants asked to perform three analytical tasks on S3D graph visualisations in a VR environment. Our findings reveal systematic relationships between network structural properties and gaze behaviour. Based on these insights, we contribute a comprehensive eye tracking methodology for analysing human perception in immersive environments and establish eye tracking as a valuable tool for objectively evaluating cognitive load in S3D graph visualisation.

Cite As Get BibTex

Lin Zhang, Yao Wang, Ying Zhang, Wilhelm Kerle-Malcharek, Karsten Klein, Falk Schreiber, and Andreas Bulling. Towards a Better Understanding of Graph Perception in Immersive Environments. In 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 357, pp. 11:1-11:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.GD.2025.11

Author Details

Lin Zhang
  • University of Stuttgart, Germany
Yao Wang
  • University of Stuttgart, Germany
Ying Zhang
  • University of Konstanz, Germany
Wilhelm Kerle-Malcharek
  • University of Konstanz, Germany
Karsten Klein
  • University of Konstanz, Germany
Falk Schreiber
  • University of Konstanz, Germany
  • Monash University, Melbourne, Australia
Andreas Bulling
  • University of Stuttgart, Germany

Funding

This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project-ID 251654672 - TRR 161 (Project A07, A09, and D04).

Acknowledgements

This project started at the SFB-TRR 161 Hackathon 2025.

Supplementary Materials

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