Animation, Small Multiples, and the Effect of Mental Map Preservation in Dynamic Graphs

Area of Science:

• Human-computer interaction
• Information visualization
• Graph theory

Background:

• Dynamic graphs are crucial for visualizing evolving data.
• Comparing visualization techniques like animation and small multiples is essential for effective data analysis.
• Understanding mental map preservation's impact on user performance is key.

Purpose of the Study:

• To compare the performance of dynamic graph animation versus small multiples.
• To evaluate the effect of mental map preservation on user comprehension.
• To assess task completion time and error rates for different visualization methods.

Main Methods:

• Conducted a human-computer interaction experiment.
• Utilized benchmark datasets from the information visualization community.
• Designed questions to test local and global properties of graph evolution.

Main Results:

• Small multiples significantly outperformed animation in overall performance and speed across five graph comprehension tasks.
• Animation resulted in significantly fewer errors than small multiples for identifying node/edge additions within timeslices.
• Mental map preservation had minimal impact on response time and error rates for both animation and small multiples.

Conclusions:

• Small multiples are generally faster for dynamic graph comprehension.
• Animation may be superior when accuracy is prioritized over speed, particularly for detailed change tracking.
• Mental map preservation strategies showed limited benefit in this study.