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**Visualizing Dynamic Networks with Matrix Cubes**

CHI 2014 Benjamin Bach, Emmanuel Pietriga, Jean-Daniel Fekete

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**Outline Introduction Related Works Cubix Real Cases Demo Conclusion**

Matrix Cube Real Cases Demo Conclusion

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Introduction designing visualizations of dynamic networks is challenging the data sets tend to be complex the Matrix Cube, a novel visual representation and navigation model for dynamic networks these manipulations can produce a range of different 2D visualizations compared to node-link diagrams, adjacency matrices are better suited to the visualization of dense networks

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**Related Works Node-link diagrams Adjacency Matrices**

The Space-Time-Cube Metaphor

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Node-link diagrams Dynamic network visualization in 1.5D. In Proc. PacificVis, IEEE (2011) Visualizing the evolution of community structures in dynamic social networks. In Proc. EuroVis, Eurographics Assoc. (2011)

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Adjacency Matrices ID 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6

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Adjacency Matrices NetVisia TimeMatrix Flowstrates

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**The Space-Time-Cube Metaphor**

GeoTime GraphDice

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**The Space-Time-Cube Metaphor**

Visualising changes in fund manager holdings in two and a half-dimensions. Information Visualization 3, 4 (2004) Visual unrolling of network evolution and the analysis of dynamic discourse. Information Visualization 2, 1 (2003)

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Matrix Cube

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**Cubix Cubix is based on the following design principles: 3D as a pivot**

Animated transitions Limited number of views Easy navigation

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Cubix UI screenshot

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**Cubix View design space**

Cubix currently implements seven predefined views

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**Cell color and size Time-projection view cell color: time**

cell size: edge weight constant cell size cell color: edge weight constant cell size cell color: frequence

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**Temporal trends in the vertex projection view**

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**Time slices juxtaposed in the timeslices view**

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**Rotation of a vertex slice**

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**Vertex small multiples view illustrating both color mappings**

cell color: edge weight cell color: timeindex

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ALMA dataset The Atacama Large Millimeter/submillimeter Array

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**Brain connectivity data**

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Conclusion The cube metaphor was quickly understood. It helped, along with animated transitions, to switch between views. Filtering, brushing and linking were useful. Cell color was always mapped to edge value. Row and column reordering are useful, but can be confusing. Matrix Cubes succeeded in providing a legible visualization where other approaches had failed so far.

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Chapter 3 Special Section Focus on Karnaugh Maps.

Chapter 3 Special Section Focus on Karnaugh Maps.

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