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Volume-based large dynamic graph analysis supported by evolution provenance

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Abstract

We present an approach for the visualization and interactive analysis of dynamic graphs that contain a large number of time steps. A specific focus is put on the support of analyzing temporal aspects in the data. Central to our approach is a static, volumetric representation of the dynamic graph based on the concept of space-time cubes that we create by stacking the adjacency matrices of all time steps. The use of GPU-accelerated volume rendering techniques allows us to render this representation interactively. We identified four classes of analytics methods as being important for the analysis of large and complex graph data, which we discuss in detail: data views, aggregation and filtering, comparison, and evolution provenance. Implementations of the respective methods are presented in an integrated application, enabling interactive exploration and analysis of large graphs. We demonstrate the applicability, usefulness, and scalability of our approach by presenting two examples for analyzing dynamic graphs. Furthermore, we let visualization experts evaluate our analytics approach.

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Acknowledgements

We would like to thank the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) for support within Projects A02, B01, and D03 of SFB/Transregio 161 (project number 251654672).

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Authors and Affiliations

  1. University of Stuttgart, Stuttgart, Germany

    Valentin Bruder, Houssem Ben Lahmar, Marcel Hlawatsch, Steffen Frey, Daniel Weiskopf, Melanie Herschel & Thomas Ertl

  2. Eindhoven University of Technology, Eindhoven, Netherlands

    Michael Burch

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  1. Valentin Bruder
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  2. Houssem Ben Lahmar
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  4. Steffen Frey
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Correspondence to Valentin Bruder.

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Bruder, V., Ben Lahmar, H., Hlawatsch, M. et al. Volume-based large dynamic graph analysis supported by evolution provenance. Multimed Tools Appl 78, 32939–32965 (2019). https://doi.org/10.1007/s11042-019-07878-6

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  • DOI: https://doi.org/10.1007/s11042-019-07878-6

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