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    This study introduces SmartGD, a novel deep learning framework for graph drawing. SmartGD optimizes multiple graph layout aesthetics, including non-differentiable criteria, outperforming existing methods.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Data Visualization

    Background:

    • Existing graph drawing methods often optimize single aesthetic criteria, leading to suboptimal layouts.
    • Few methods flexibly optimize diverse aesthetic aspects using different criteria.
    • Recent deep learning approaches show promise but struggle with non-differentiable criteria.

    Purpose of the Study:

    • To develop a flexible deep learning framework for graph drawing that optimizes multiple aesthetic goals, including non-differentiable ones.
    • To address limitations of current methods in handling diverse and non-differentiable aesthetic criteria.

    Main Methods:

    • A novel Generative Adversarial Network (GAN) based deep learning framework named SmartGD was developed.
    • The framework is designed to optimize various quantitative aesthetic goals, irrespective of their differentiability.

    Main Results:

    • SmartGD demonstrated effectiveness in minimizing stress and edge crossings.
    • The framework successfully maximized crossing angles and shape-based metrics.
    • Experiments showed SmartGD's ability to optimize combinations of multiple aesthetics.

    Conclusions:

    • SmartGD offers a powerful and flexible solution for graph drawing, optimizing diverse aesthetic criteria.
    • The proposed GAN-based framework achieves competitive quantitative and qualitative performance compared to existing algorithms.