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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
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Related Experiment Video

Updated: May 7, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

DiffAni: visualizing dynamic graphs with a hybrid of difference maps and animation.

Sébastien Rufiange¹, Michael J McGuffin

¹école de technologie supérieure.

IEEE Transactions on Visualization and Computer Graphics

|September 21, 2013

Summary

This summary is machine-generated.

Visualizing dynamic graphs is challenging. A new hybrid approach combines difference maps, animation, and small multiples to overcome limitations of existing methods, showing benefits in specific scenarios.

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Area of Science:

Computer Science
Information Visualization

Background:

Visualizing dynamic networks (graphs) presents significant research challenges.
Existing visualization techniques like animation and small multiples have inherent trade-offs.

Purpose of the Study:

To introduce novel taxonomies for dynamic graph visualizations.
To present a hybrid visualization prototype, DiffAni, addressing limitations of non-hybrid methods.

Main Methods:

Developed two taxonomies: one for non-hybrid and one for hybrid dynamic graph visualizations.
Created DiffAni, a prototype visualizing dynamic graphs using a sequence of time-ordered tiles (diff, animation, small multiples).

Main Results:

The hybrid approach, DiffAni, was experimentally evaluated.
Results indicate that the hybrid visualization strategy offers advantages over non-hybrid techniques in specific use cases.

Conclusions:

Hybrid visualization techniques can mitigate trade-offs found in traditional dynamic graph visualizations.
DiffAni demonstrates the potential of combining different visualization methods for improved dynamic graph analysis.