EuroVisPosters2021

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https://diglib.eg.org/handle/10.2312/2633049

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Browsing EuroVisPosters2021 by Subject "Visualization techniques"

Now showing 1 - 2 of 2
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    Elastic Tree Layouts for Interactive Exploration of Mentorship
    (The Eurographics Association, 2021) Yan, Xin Yuan; Ma, Yi Fang; Byška, Jan and Jänicke, Stefan and Schmidt, Johanna
    Mentorship is an important collaborative relationship among scholars. The existing tools to visualize it mainly suffer from a waste of space, lack of overview representation, and less displayed attribute information. To solve these problems, we propose a novel elastic tree layout based on node-link diagrams, in which nodes and edges are represented as elastic rectangles and bands respectively. By stretching, compressing, aggregating, and expanding nodes and edges, we can: get a compact tree layout with high space-efficiency, display both the detailed subtree and compressed context in a single view, use labeling, charts, and node opacity to show multiple attributes. Besides, we designed various animated interactions to facilitate the exploration.
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    Unfolding Edges for Exploring Multivariate Edge Attributes in Graphs
    (The Eurographics Association, 2021) Bludau, Mark-Jan; Dörk, Marian; Tominski, Christian; Byška, Jan and Jänicke, Stefan and Schmidt, Johanna
    With this research we present an approach to network visualization that expands the capabilities for visual encoding and interactive exploration through edges in node-link diagrams. Compared to the various possibilities for visual and interactive properties of nodes, there are few techniques for interactive visualization of multivariate edge attributes in node-link diagrams. Visualization of edge attributes is oftentimes limited by occlusion and space issues of methods that globally encode attributes in a node-link diagram for all edges, not sufficiently exploiting the potential of interaction. Building up on existing techniques for edge encoding and interaction, we propose 'Unfolding Edges' as an exemplary use of an on-demand detail enhancing approach for exploration of multivariate edge attributes.