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Edge compression techniques for visualization of dense directed graphs.

Tim Dwyer¹, Nathalie Henry Riche, Kim Marriott

¹Monash University.

IEEE Transactions on Visualization and Computer Graphics

|September 21, 2013

Summary

This summary is machine-generated.

Visualizing complex graphs involves compressing edges using modules. While this reduces visual clutter, more advanced techniques like Power Graph Analysis may increase interpretation difficulty for users.

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Area of Science:

Computer Science
Information Visualization
Graph Theory

Background:

Dense directed graphs present visualization challenges.
Edge compression techniques aim to simplify graph representation.
Lossless compression ensures original graph data integrity.

Purpose of the Study:

Evaluate techniques for visualizing dense directed graphs.
Investigate the trade-off between edge compression and user interpretation effort.
Explore modularization methods for graph visualization.

Main Methods:

Compared three lossless graph compression techniques: node grouping, Modular Decomposition, and Power Graph Analysis.
Conducted two user studies to assess mental effort required for interpretation.
Developed a novel optimal Power Graph Analysis technique using constraint programming.

Main Results:

Successive relaxation of module definitions (grouping to Power Graph Analysis) reduces the number of edges to render.
Hypothesized increase in mental effort for interpreting more complex techniques was tested.
Novel constraint programming approach enabled precise exploration of Power Graph Analysis parameters.

Conclusions:

Modularization techniques offer effective lossless compression for dense directed graphs.
A potential trade-off exists between visual simplification and cognitive load.
Power Graph Analysis, particularly with optimal techniques, shows promise for software dependency analysis visualization.