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Visualizing Dynamic Networks with Matrix Cubes

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Presentation on theme: "Visualizing Dynamic Networks with Matrix Cubes"— Presentation transcript:

1 Visualizing Dynamic Networks with Matrix Cubes
CHI 2014 Benjamin Bach, Emmanuel Pietriga, Jean-Daniel Fekete

2 Outline Introduction Related Works Cubix Real Cases Demo Conclusion
Matrix Cube Real Cases Demo Conclusion

3 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

4 Related Works Node-link diagrams Adjacency Matrices
The Space-Time-Cube Metaphor

5 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)

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

7 Adjacency Matrices NetVisia TimeMatrix Flowstrates

8 The Space-Time-Cube Metaphor
GeoTime GraphDice

9 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)

10 Matrix Cube

11 Cubix Cubix is based on the following design principles: 3D as a pivot
Animated transitions Limited number of views Easy navigation

12 Cubix UI screenshot

13 Cubix View design space
Cubix currently implements seven predefined views

14 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

15 Temporal trends in the vertex projection view

16 Time slices juxtaposed in the timeslices view

17 Rotation of a vertex slice

18 Vertex small multiples view illustrating both color mappings
cell color: edge weight cell color: timeindex

19 ALMA dataset The Atacama Large Millimeter/submillimeter Array

20 Brain connectivity data

21 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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