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CUBu: Universal Real-Time Bundling for Large Graphs.

Matthew van der Zwan, Valeriu Codreanu, Alexandru Telea

    IEEE Transactions on Visualization and Computer Graphics
    |January 14, 2016
    PubMed
    Summary

    We developed CUBu, a GPU-based framework for visualizing large graphs using edge bundling. CUBu significantly improves speed and reduces clutter for complex network visualizations.

    Area of Science:

    • Computer Science
    • Data Visualization
    • Graph Theory

    Background:

    • Edge bundling is a key technique for visualizing large graphs.
    • Existing methods face challenges in speed, clutter, level-of-detail, and parameter control.

    Purpose of the Study:

    • To present CUBu, an integrated framework addressing challenges in large graph visualization.
    • To enable interactive visualization of graphs with up to one million edges.

    Main Methods:

    • A fully GPU-based framework (CUBu) for edge bundling.
    • Integrated approach to control bundle shapes, directionality, and level-of-detail shading.
    • Interactive parameter control for bundling.

    Main Results:

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  • CUBu achieves interactive framerates for graphs up to one million edges.
  • Demonstrates over 50x speed improvement compared to state-of-the-art methods.
  • Successfully visualizes large, real-world graphs, revealing core structures and details.

    • Conclusions:

    • CUBu offers an efficient and intuitive solution for large graph visualization.
    • The framework unifies and extends existing edge bundling techniques.
    • Enables detailed and clear visualization of complex network data.