This presentation is titled 'Reading Strategies for Graph Visualisations that Wrap Around in Torus Topology'. It was presented at ETRA 2023, and was a collaboration between the University of Adelaide, Monash University and Universitat Stuttgart.

1. Reading Strategies for Graph Visualizations that
Wrap Around in Torus Topology
Kun-Ting Chen, Quynh Quang Ngo, Kuno Kurzhals, Kim Marriott, Tim Dwyer,
Michael Sedlmair, Daniel Weiskopf
URL: https://kun-ting.github.io/
Email: kun-ting.chen@adelaide.edu.au

2. Introduction
• Node-link diagrams that wrap around the boundaries in a 2D-
projected torus topology
• Eye tracking to understand users' visualization
task solution strategies.
2
DoughNets [Chen et al. 2020]

3. Background: An Example of Torus-based
Network Layout
1 2
3
4
5
6
7
8
9
3

4. Related Work
4
DoughNets, [Chen et
al. 2020]
Geodesic-path
tendency [Huang et al. 2009]​
Visual task solution
strategies for node-link trees
[Burch et al. 2013]​

5. Methodology
• Experimental Protocol
• 24 participants, 72 layouts (4 techniques) per participant
• Within-subject design
• Tutorial -> training -> trials -> subjective user feedback
• Tobii Pro X3-120
• Visualization tasks: Link-and-Path following
• Data analysis
• Exploratory visual analysis (EVA) using Gazealytics
• Statistical tests
5

6. Tasks
• What is the shortest path between Start and End
PartialContext
NoContext
FullContext
NoTorus
6

7. Prior Results of Task Performance
7
PartialContext
NoContext
NoTorus
FullContext

8. Exploratory Visual Analysis (EVA) using
Gazealytics
8
1: Time-aggregated
fixation distribution
2: Reading strategies based on
scanpath analysis
3: Comparative statistical
analysis based on gaze
metrics

9. Results-1: Time-aggregated Fixation Distribution
• NoTorus: single
cluster of fixations
• NC, PC, FC: clearer
cluster separation
for wrapped trials
• FC: dense
fixations at the
center, sparse
outward
9

10. Results-1: Time-aggregated Fixation Distribution
10
• NoTorus: single
cluster of fixations
• NC, PC, FC: clearer
cluster separation
for wrapped trials
• FC: dense
fixations at the
center, sparse
outward

11. Results-2.1: Reading Behaviors Informed by Scanpaths
11
• NC: increasing
frequency of
cross-checking
• PC: ineffective
visual search
at times
• FC: successful
scan around
the center

12. Exploratory Visual Analysis using Gazealytics
• Saccades analysis
12
• Gazealytics is an open-source toolkit
• Live instance: https://www2.visus.uni-stuttgart.de/gazealytics/

13. Results-2.2: Saccades Analysis
13

14. Result-3: Comparative statistical analysis
based on gaze metrics

15. Results-4: Gaze Allocation of Full-Context
Layout
Center, Outer1 have both have significantly higher gaze
allocation (AOI percentage time) than Outer 2 and Outer 3

16. Implication for Design
• Full-Context: could be more space efficient based on gaze allocation for
link-path-following task
• Partial-Context: increase the replication and avoid graph layout with
ineffective visual searches

17. Conclusion & Future Work
• Distinguishable reading behaviors for graphs that wrap around in torus
topology
• EVA using Gazealytics
• Open-source URL
• Live instance: https://www2.visus.uni-stuttgart.de/gazealytics/
• Future work
• Eye tracking controlled study with larger sample size with both high-level and low-
level graph exploration tasks
• Design more effective torus wrapping visualizations for graphs as well as cyclic data
applications
17

18. Reading Strategies for Graph Visualizations that
Wrap Around in Torus Topology
Kun-Ting Chen, Quynh Quang Ngo, Kuno Kurzhals, Kim Marriott, Tim Dwyer,
Michael Sedlmair, Daniel Weiskopf
URL: https://kun-ting.github.io/
Email: kun-ting.chen@adelaide.edu.au
Thank you