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Michael Behrisch, Tobias Schreck, Hanspeter Pfister

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    Summary
    This summary is machine-generated.

    GUIRO, a visual analytics system, enhances understanding of matrix reordering algorithms for network data. It improves transparency and user-guided steering, aiding both novices and experts in data and algorithm insights.

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    Area of Science:

    • Computer Science
    • Data Visualization
    • Human-Computer Interaction

    Background:

    • Matrix representations are effective for visualizing relational data, but their effectiveness depends on layout algorithms.
    • Choosing the right matrix reordering algorithm is challenging due to diverse outputs and lack of transparency in automated processes.

    Purpose of the Study:

    • To present GUIRO, a Visual Analytics system designed to demystify matrix reordering algorithms.
    • To provide tools for investigating algorithm expressiveness, trustworthiness, and enabling user-guided reordering.

    Main Methods:

    • GUIRO offers access to 70 matrix reordering algorithms and 16 quality metrics.
    • Introduces a novel model space representation and interaction techniques for user-guided reordering, including submatrix reordering.
    • Supports comparison of reordering implementations at a row/column permutation level.

    Main Results:

    • GUIRO helps users, including novices, understand matrix patterns and the impact of different reordering algorithms.
    • User-guided steering of reordering algorithms is facilitated, improving insight into data topology.
    • Increased transparency of automated reordering processes was observed.

    Conclusions:

    • GUIRO enhances the transparency and explainability of matrix reordering algorithms.
    • The system empowers a broad range of users to gain deeper insights into relational data and reordering techniques.
    • GUIRO supports both data analysis and the development of new reordering algorithms.