Visualising the Transition of Large Networks via Dimensionality Reduction to Illustrate the Evolution of the Human Brain

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dc.contributor.authorGanglberger, Florianen_US
dc.contributor.authorKaczanowska, Joannaen_US
dc.contributor.authorHaubensak, Wulfen_US
dc.contributor.authorBühler, Katjaen_US
dc.contributor.editorTheisel, Holger and Wimmer, Michaelen_US
dc.date.accessioned2021-04-09T18:20:21Z
dc.date.available2021-04-09T18:20:21Z
dc.date.issued2021
dc.description.abstractAdvances in high-throughput imaging techniques enable the creation of networks depicting spatio-temporal biological and neurophysiological processes with unprecedented size and magnitude. These networks involve thousands of nodes, which can not be compared over time by traditional methods due to complexity and clutter. When investigating networks over multiple time steps, a crucial question for the visualisation research community becomes apparent: How to visually trace changes of the connectivity over several transitions? Therefore, we developed an easy-to-use method that maps multiple networks to a common embedding space. Visualising the distribution of node-clusters of interest (e.g. brain regions) enables their tracing over time. We demonstrate this approach by visualizing spatial co-evolution networks of different evolutionary timepoints as small multiples to investigate how the human brain genetically and functionally evolved over the mammalian lineage.en_US
dc.description.sectionheadersModeling and Rendering
dc.description.seriesinformationEurographics 2021 - Short Papers
dc.identifier.doi10.2312/egs.20211014
dc.identifier.isbn978-3-03868-133-5
dc.identifier.issn1017-4656
dc.identifier.pages21-24
dc.identifier.urihttps://doi.org/10.2312/egs.20211014
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/egs20211014
dc.publisherThe Eurographics Associationen_US
dc.titleVisualising the Transition of Large Networks via Dimensionality Reduction to Illustrate the Evolution of the Human Brainen_US
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