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dc.contributor.authorAbdelaal, Moatazen_US
dc.contributor.authorHlawatsch, Marcelen_US
dc.contributor.authorBurch, Michaelen_US
dc.contributor.authorWeiskopf, Danielen_US
dc.contributor.editorBeck, Fabian and Dachsbacher, Carsten and Sadlo, Filipen_US
dc.date.accessioned2018-10-10T04:52:18Z
dc.date.available2018-10-10T04:52:18Z
dc.date.issued2018
dc.identifier.isbn978-3-03868-072-7
dc.identifier.urihttps://doi.org/10.2312/vmv.20181262
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vmv20181262
dc.description.abstractWe present a time-scalable approach for visualizing dynamic graphs. By adopting bipartite graph layouts known from parallel edge splatting, individual graphs are horizontally stacked by drawing partial edges, leading to stacked edge splatting. This allows us to uncover the temporal patterns together with achieving time-scalability. To preserve the graph structural information, we introduce the representative graph where edges are aggregated and drawn at full length. The representative graph is then placed on the top of the last graph in the (sub)sequence. This allows us to obtain detailed information about the partial edges by tracing them back to the representative graph. We apply sequential temporal clustering to obtain an overview of different temporal phases of the graph sequence together with the corresponding structure for each phase. We demonstrate the effectiveness of our approach by using real-world datasets.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectInformation visualization
dc.subjectVisual analytics
dc.titleClustering for Stacked Edge Splattingen_US
dc.description.seriesinformationVision, Modeling and Visualization
dc.description.sectionheadersInformation and Geographic Visualization
dc.identifier.doi10.2312/vmv.20181262
dc.identifier.pages127-134


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  • VMV18
    ISBN 978-3-03868-072-7

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