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e-Xploration
antropologo.net, dataviz, collective intelligence, algorithms, social learning, social change, digital humanities
Curated by luiy
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Rescooped by luiy from Complex Networks
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Visualization techniques for categorical analysis of social networks with multiple edge sets | #SNA

Visualization techniques for categorical analysis of social networks with multiple edge sets | #SNA | e-Xploration | Scoop.it

Via Becheru Alexandru
luiy's insight:

The node link graph on the left runs into limitation when trying to compare multiple properties, since only one property can be mapped to color at a time. This makes it hard for the user to look at both gender and grade level. In the radial layout on the right, we group by grade and map color to gender. The visualization starts with 8th grade on top and continues counter-clockwise with 12th grade at bottom right and unknown to the top right. The radial layout shows that gender plays less of a role as kids get older (there is more mixing of gender in higher grades).

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Divided Edge Bundling for Directional Network Data I #SNA #dataviz

Divided Edge Bundling for Directional Network Data I #SNA #dataviz | e-Xploration | Scoop.it
luiy's insight:

The node-link diagram is an intuitive and venerable way to depict a graph. To reduce clutter and improve the readability of node-link views, Holten & van Wijk's force-directed edge bundling employs a physical simulation to spatially group graph edges. While both useful and aesthetic, this technique has shortcomings: it bundles spatially proximal edges regardless of direction, weight, or graph connectivity. As a result, high-level directional edge patterns are obscured. We present divided edge bundling to tackle these shortcomings. By modifying the forces in the physical simulation, directional lanes appear as an emergent property of edge direction. By considering graph topology, we only bundle edges related by graph structure. Finally, we aggregate edge weights in bundles to enable more accurate visualization of total bundle weights. We compare visualizations created using our technique to standard force-directed edge bundling, matrix diagrams, and clustered graphs; we find that divided edge bundling leads to visualizations that are easier to interpret and reveal both familiar and previously obscured patterns.

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