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    This study introduces a new graph visualization pipeline that combines graph structure and attributes for better exploration. The GEGraph algorithm creates aesthetically pleasing layouts that preserve community structures, improving graph interpretation.

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    Area of Science:

    • Computer Science
    • Data Visualization
    • Graph Theory

    Background:

    • Node-link diagrams are essential for graph visualization, but existing layout algorithms struggle to balance aesthetic goals (e.g., minimizing edge crossings) with exploration goals (e.g., preserving community structures).
    • Current hybrid methods often face limitations such as restricted input types, manual adjustments, and a lack of balance between visual appeal and analytical insight.

    Purpose of the Study:

    • To propose a flexible embedding-based graph exploration pipeline that effectively integrates graph topology and node attributes.
    • To develop an embedding-driven graph layout algorithm (GEGraph) that enhances both aesthetic quality and community preservation for improved graph interpretation.
    • To extend graph exploration capabilities through layout-preserving aggregation and proximity-based node searching.

    Main Methods:

    • Leveraged embedding algorithms for attributed graphs to encode both graph topology and node attributes into a latent space.
    • Developed GEGraph, an embedding-driven graph layout algorithm designed for aesthetic appeal and superior community preservation.
    • Implemented layout-preserving aggregation with Focus+Context interaction and a related nodes searching approach utilizing multiple proximity strategies.

    Main Results:

    • The proposed pipeline successfully integrates graph topology and node attributes, overcoming limitations of existing methods.
    • GEGraph generates aesthetically pleasing graph layouts that effectively preserve community structures, facilitating easier interpretation.
    • The developed exploration methods enhance usability and provide deeper insights into graph data.

    Conclusions:

    • The embedding-based graph exploration pipeline offers a flexible and effective approach to visualizing and analyzing attributed graphs.
    • GEGraph and associated exploration tools provide a significant advancement in balancing aesthetic layout goals with analytical requirements for graph data.
    • Evaluations, including a user study and case studies, validate the approach's effectiveness and practical utility.