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Influence Maximization With Visual Analytics.

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    VAIM is a visual analytics system designed to help analyze influence spread in social networks. It aids in evaluating and comparing influence maximization algorithms, improving seed set selection for better results.

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    Area of Science:

    • Social Network Analysis
    • Information Diffusion Modeling
    • Visual Analytics

    Background:

    • Social networks facilitate product promotion and opinion dissemination, making influence maximization crucial.
    • The Influence Maximization (IM) problem, selecting seed users to maximize spread, is NP-hard and complex due to stochastic diffusion.
    • Existing heuristics for IM often lack insights into network topology's impact on diffusion.

    Purpose of the Study:

    • To introduce VAIM, a visual analytics system for analyzing, evaluating, and comparing information diffusion processes from IM algorithms.
    • To provide insights for modifying seed sets to enhance influence spread.
    • To support users in understanding and improving IM algorithm performance.

    Main Methods:

    • Developed VAIM, a visual analytics system tailored for influence maximization analysis.
    • Conducted a qualitative evaluation with domain experts using two datasets.
    • Performed a quantitative assessment using the ICE-T methodology.

    Main Results:

    • VAIM effectively supports users in the visual analysis of IM algorithm performance.
    • The system provides actionable insights for improving seed set selection.
    • Both qualitative and quantitative assessments validated the system's utility.

    Conclusions:

    • VAIM enhances the analysis and comparison of influence maximization algorithms.
    • The system aids in optimizing seed set selection for greater influence spread.
    • VAIM offers valuable visual insights into information diffusion dynamics in social networks.