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    Eiffel visualizes complex, time-evolving influence graphs by summarizing them across nodes, relations, and time. This system enhances understanding of citation and social influence dynamics.

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    Area of Science:

    • Information Visualization
    • Data Science
    • Network Analysis

    Background:

    • Visualizing evolutionary influence graphs is crucial for tasks like citation and social influence analysis.
    • Challenges include summarizing large, complex, and time-varying graph data.
    • Effective visual metaphors and dynamic representations are needed to show temporal influence patterns.

    Purpose of the Study:

    • To present Eiffel, an integrated visual analytics system for evolutionary influence graphs.
    • To address challenges in summarizing and visualizing dynamic influence data.
    • To evaluate the effectiveness of Eiffel in analyzing influence graph dynamics.

    Main Methods:

    • Eiffel employs triple summarizations across nodal, relational, and temporal dimensions.
    • A flow map representation is adapted for influence graph summarization.
    • Two evolutionary visualization modes (flip-book and movie) are supported.

    Main Results:

    • Eiffel's summarization outperformed traditional clustering algorithms in numerical experiments.
    • User experiments validated Eiffel's effectiveness for influence graph summarization and visualization.
    • The system successfully analyzed citation and social influence in real-world scenarios.

    Conclusions:

    • Eiffel provides a valuable tool for the visual analysis of evolutionary influence graphs.
    • The system effectively summarizes and visualizes complex, dynamic influence data.
    • Eiffel aids in understanding citation and social influence patterns over time.