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    This study introduces a novel abstraction method using graphons to visualize complex, large multi-layer networks. The technique effectively manages intricate cloud computing systems and other network types on standard hardware.

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    Area of Science:

    • Computer Science
    • Network Science
    • Data Visualization

    Background:

    • Traditional graph visualization faces challenges with large-scale cloud computing due to heterogeneity, scalability, and data incompleteness.
    • Effective visualization is crucial for managing cloud system availability and reliability.

    Purpose of the Study:

    • To propose a novel abstraction approach for visualizing large multi-layer networks.
    • To address the limitations of traditional graph visualization in complex, large-scale systems.

    Main Methods:

    • Leveraging graphons for probabilistic representation of network layers.
    • Employing an inner-layer summary for substructure identification.
    • Utilizing inter-layer mixup for aligning heterogeneous layers.
    • Implementing context-aware multi-layer joint sampling to reduce network scale while preserving topology.

    Main Results:

    • Abstraction of complex network data into manageable weighted graphs representing distinct network layers.
    • Demonstrated effectiveness in managing large multi-layer networks, including cloud computing systems.
    • Validation through case studies, quantitative experiments, and expert evaluations.

    Conclusions:

    • The proposed graphon-based abstraction method enhances the accessibility of complex network data.
    • The approach is effective for visualizing large multi-layer networks and applicable to diverse network types like transportation and social networks.