Volume-based large dynamic graph analysis supported by evolution provenance

Valentin Bruder (Corresponding author), Houssem Ben Lahmar, Marcel Hlawatsch, Steffen Frey, Michael Burch, Daniel Weiskopf, Melanie Herschel, Thomas Ertl

Research output: Contribution to journalArticleAcademicpeer-review


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.

Original languageEnglish
Pages (from-to)32939-32965
Number of pages27
JournalMultimedia Tools and Applications
Issue number23
Early online date29 Jun 2019
Publication statusPublished - 1 Dec 2019


  • Dynamic graphs
  • Evolution provenance
  • Visual analytics
  • Volume rendering


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