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AutoFDP: Automatic Force-Based Model Selection for Multicriteria Graph Drawing.

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    This study introduces criteria-driven force-based graph layout techniques, integrating physics-based and readability-focused methods. The new framework generates superior graph visualizations adaptable to user needs.

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    Area of Science:

    • Computer Science
    • Graph Theory
    • Data Visualization

    Background:

    • Traditional graph layout methods rely on physics simulations or direct optimization of readability criteria.
    • A gap exists in systematically integrating these two distinct approaches for enhanced graph visualization.

    Purpose of the Study:

    • To develop and evaluate criteria-driven force-based graph layout techniques.
    • To propose a general framework for automatically constructing force-based models based on user-defined readability criteria.

    Main Methods:

    • Developed a novel framework integrating force-based models with user-specified readability criteria (e.g., minimizing edge crossings).
    • Enabled reuse of models from similar graphs and refinement of layouts on subgraphs.
    • Conducted quantitative comparisons with existing layout techniques and a case study on graph exploration.

    Main Results:

    • The proposed criteria-driven force-based methods generated superior graph layouts compared to traditional techniques.
    • The framework demonstrated better generalization capabilities than deep learning-based graph layout methods.
    • Effectively tailored force-based models to specific graph structures and user requirements.

    Conclusions:

    • Criteria-driven force-based graph layout offers a powerful and flexible approach to graph visualization.
    • The proposed framework enhances layout quality and adaptability, outperforming existing methods.
    • This integration provides a promising direction for future research in automated graph layout.