Visualizing a Sequence of a Thousand Graphs (or Even More)

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dc.contributor.authorBurch, Michaelen_US
dc.contributor.authorHlawatsch, Marcelen_US
dc.contributor.authorWeiskopf, Danielen_US
dc.contributor.editorHeer, Jeffrey and Ropinski, Timo and van Wijk, Jarkeen_US
dc.date.accessioned2017-06-12T05:22:38Z
dc.date.available2017-06-12T05:22:38Z
dc.date.issued2017
dc.description.abstractThe visualization of dynamic graphs demands visually encoding at least three major data dimensions: vertices, edges, and time steps. Many of the state-of-the-art techniques can show an overview of vertices and edges but lack a data-scalable visual representation of the time aspect. In this paper, we address the problem of displaying dynamic graphs with a thousand or more time steps. Our proposed interleaved parallel edge splatting technique uses a time-to-space mapping and shows the complete dynamic graph in a static visualization. It provides an overview of all data dimensions, allowing for visually detecting timevarying data patterns; hence, it serves as a starting point for further data exploration. By applying clustering and ordering techniques on the vertices, edge splatting on the links, and a dense time-to-space mapping, our approach becomes visually scalable in all three dynamic graph data dimensions. We illustrate the usefulness of our technique by applying it to call graphs and US domestic flight data with several hundred vertices, several thousand edges, and more than a thousand time steps.en_US
dc.description.number3
dc.description.sectionheadersGraph Visualization
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume36
dc.identifier.doi10.1111/cgf.13185
dc.identifier.issn1467-8659
dc.identifier.pages261-271
dc.identifier.urihttps://doi.org/10.1111/cgf.13185
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13185
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectData Structures [E.1]
dc.subjectGraphs and Networks
dc.titleVisualizing a Sequence of a Thousand Graphs (or Even More)en_US
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