This book is the outcome of the Dagstuhl Seminar 13201 on Information Visualization - Towards Multivariate Network Visualization, ...
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This book is the outcome of the Dagstuhl Seminar 13201 on Information Visualization - Towards Multivariate Network Visualization, held in Dagstuhl Castle, Germany in May 2013. The goal of this Dagstuhl Seminar was to bring together theoreticians and practitioners from Information Visualization, HCI and Graph Drawing with a special focus on multivariate network visualization, i.e., on graphs where the nodes and/or edges have additional (multidimensional) attributes. The integration of multivariate data into complex networks and their visual analysis is one of the big challenges not only in visualization, but also in many application areas. Thus, in order to support discussions related to the visualization of real world data, also invited researchers from selected application areas, especially bioinformatics, social sciences and software engineering. The unique "Dagstuhl climate" ensured an open and undisturbed atmosphere to discuss the state-of-the-art, new directions and open challenges of multivariate network visualization.
Presents and extends the findings of Dagstuhl Seminar no. 13201 on Information Visualization Chapters are collaboratively written by experts in information visualization, HCI and graph drawing with a special focus on multivariate network visualization Covers a variety of topics related to multivariate network visualization and its applications in software engineering, social networks, life sciences Inhalt Introduction to Multivariate Network Visualization.- Multivariate Networks in Software Engineering.- Multivariate Social Network Visual Analytics.- Multivariate Networks in the Life Sciences.- Tasks for Multivariate Network Analysis.- Interaction in the Visualization of Multivariate Networks.- Novel Visual Metaphors for Multivariate Networks.- Temporal Multivariate Networks.- Heterogeneous Networks on Multiple Levels.- Scalability Considerations for Multivariate Graph Visualization.