The Sprawlter Graph Readability Metric: Combining Sprawl and Area-Aware Clutter.

Area of Science:

• Computer Science
• Graph Theory
• Data Visualization

Background:

• Graph drawing readability metrics are essential for network data visualization.
• Current metrics often fail to capture fine-grained overlap details and multi-level structures.
• Existing metrics do not account for the trade-off between clutter and information sprawl.

Purpose of the Study:

• To propose novel, area-aware graph drawing readability metrics.
• To introduce "sprawlter" metrics combining clutter and sprawl measurements.
• To address limitations of existing count-based and single-level metrics.

Main Methods:

• Developed area-aware clutter metrics for detailed geometric overlap quantification.
• Incorporated handling of variable-sized nodes and uniform treatment of metanodes and leaf nodes.
• Defined "sprawlter" metrics by combining area-aware clutter with a sprawl metric and discussed penalty mapping functions.

Main Results:

• Proposed metrics provide a more nuanced assessment of graph layout quality.
• Validated metrics through computational analysis of diverse graph layouts and algorithms.
• Demonstrated the ability of new metrics to capture overlap extent and sprawl.

Conclusions:

• The proposed sprawlter metrics offer a more comprehensive evaluation of graph drawing readability.
• These metrics enhance the assessment of network visualizations by considering area and multi-level properties.
• The new approach provides a more accurate measure of visualization quality compared to traditional methods.