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Efficient Minimum Cost Seed Selection With Theoretical Guarantees for Competitive Influence Maximization.

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    This study introduces a new algorithm for minimum cost seed selection in competitive social networks. The proposed method efficiently identifies key users to maximize influence while offering strong performance guarantees.

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    Area of Science:

    • Computer Science
    • Network Science
    • Algorithmic Game Theory

    Background:

    • Competitive influence maximization is crucial for applications like marketing and political campaigns.
    • Existing methods struggle with computational complexity, balancing approximation guarantees and efficiency.
    • Current approaches often rely on costly simulations or heuristics with limited guarantees.

    Purpose of the Study:

    • To develop an efficient algorithm for minimum cost seed selection in competitive social networks.
    • To provide bounded approximation guarantees for influence maximization.
    • To improve the empirical efficiency of solving this computationally complex problem.

    Main Methods:

    • A novel competitive reverse influence estimation-based greedy (CRIEG) algorithm is proposed.
    • The algorithm utilizes representative sketches to avoid repeated, costly simulations.
    • It operates under the competitive independent cascade model.

    Main Results:

    • The CRIEG algorithm achieves bounded approximation guarantees.
    • It demonstrates significantly improved empirical efficiency compared to state-of-the-art methods.
    • Experiments on large real-world networks show superior speed and performance.

    Conclusions:

    • The proposed CRIEG algorithm offers an efficient and effective solution for minimum cost seed selection.
    • It overcomes the computational challenges of traditional methods.
    • The approach provides a practical tool for influence maximization in competitive environments.