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GVVST: Image-Driven Style Extraction From Graph Visualizations for Visual Style Transfer.

Sicheng Song, Yipeng Zhang, Yanna Lin

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    Summary
    This summary is machine-generated.

    This study introduces an automated method to extract and apply visual styles from well-designed node-link diagrams, reducing designer effort. The approach uses deep learning to capture global and local styles, streamlining graph visualization design.

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    Area of Science:

    • Computer Science
    • Data Visualization

    Background:

    • Graph visualization design is complex and time-consuming.
    • Manual style transfer requires significant designer effort.
    • Existing methods lack automated style extraction capabilities.

    Purpose of the Study:

    • To develop an automated approach for extracting and transferring visual styles in graph visualizations.
    • To reduce the workload of designers by streamlining the visualization creation process.
    • To identify and categorize key global and local visual styles in node-link diagrams.

    Main Methods:

    • Formative study to identify designer-considered styles (global and local).
    • Deep learning models for saliency detection and multi-label classification.
    • End-to-end pipelines for style extraction and application.

    Main Results:

    • Successful extraction of global styles (color scheme, layout) and local styles (node/edge details).
    • Demonstrated efficacy and time-saving benefits through user studies.
    • Validation of the automated style transfer approach.

    Conclusions:

    • Automated style extraction and transfer significantly aids graph visualization design.
    • The proposed method enhances efficiency and reduces designer workload.
    • Deep learning techniques offer a powerful solution for visual style automation.