Bridging Network Science and Vision Science: Mapping Perceptual Mechanisms to Network Visualization Tasks

Area of Science:

• Computer Science
• Human-Computer Interaction
• Cognitive Science

Background:

• Network visualization design often lacks a strong foundation in human perception.
• Current designs rely heavily on intuition and algorithmic optimization.
• Limited understanding of perception hinders the effectiveness and generalizability of network visualizations.

Purpose of the Study:

• To bridge the gap between human perception and network visualization design.
• To introduce a framework detailing key perceptual mechanisms in network visualization.
• To guide the development of perceptually effective network visualizations.

Main Methods:

• Developed a framework outlining five perceptual mechanisms: attention, visual search, perceptual organization, ensemble coding, and object recognition.
• Applied the framework to analyze existing empirical studies on network visualization.
• Proposed future experimental designs informed by the perceptual framework.

Main Results:

• The framework elucidates the role of perceptual mechanisms in common network analytical tasks.
• Revisited four past empirical investigations through the lens of the new framework.
• Identified opportunities for future research and design experiments.

Conclusions:

• Connecting perception and network visualization offers translational understanding for design.
• The framework provides hypotheses for developing perception-aware network visualizations.
• Future work can lead to more effective and interpretable network visualization tools.