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Related Concept Videos

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Proxy Graph: Visual Quality Metrics of Big Graph Sampling.

Quan Hoang Nguyen, Seok-Hee Hong, Peter Eades

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    Proxy graphs, created by sampling large graphs, offer efficient visualization. This study introduces new metrics to evaluate visual quality, providing guidelines for effective use in graph visualization.

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    Area of Science:

    • Computer Science
    • Data Visualization
    • Graph Theory

    Background:

    • Data sampling is crucial for efficient large-scale graph mining.
    • Proxy objects are widely used in software engineering for managing complex systems.
    • Existing research lacks evaluation of sampling techniques specifically for graph visualization.

    Purpose of the Study:

    • To empirically evaluate the effectiveness of proxy graphs, generated via sampling, in representing large graphs visually.
    • To introduce novel quality metrics for assessing the visual quality of proxy graph visualizations.
    • To provide practical guidelines for utilizing sampling-based proxy graphs in visualization.

    Main Methods:

    • Coined the term 'proxy graph' to describe sampled graph representations.
    • Developed a new family of objective quality metrics for visual assessment.
    • Conducted experiments using popular data sampling techniques on large graphs.
    • Evaluated the visual representation capabilities of sampling-based proxy graphs.

    Main Results:

    • Demonstrated that sampling-based proxy graphs can effectively represent large graphs for visualization.
    • The proposed quality metrics provide an objective measure for evaluating visual fidelity.
    • Experimental results highlight the performance of various sampling techniques in visualization contexts.

    Conclusions:

    • Sampling-based proxy graphs are a viable approach for visualizing large graphs efficiently.
    • The developed metrics and experimental findings offer valuable insights for practitioners.
    • Guidelines are provided to aid in the selection and application of sampling methods for graph visualization.