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MFEA-RCIM: A Multifactorial Evolutionary Algorithm for Determining Robust and Influential Seeds From Competitive

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    This study introduces multitask optimization for the robust competitive influence maximization (RCIM) problem. A new algorithm, MFEA-RCIM, effectively balances diverse group propagation for better network performance.

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    Area of Science:

    • Network Science
    • Computational Social Science
    • Optimization

    Background:

    • Networks are crucial for understanding system dynamics and information flow.
    • The robust competitive influence maximization (RCIM) problem seeks optimal seed sets for propagation in networks.
    • Existing methods lack a comprehensive approach to balancing diverse group influences in RCIM.

    Purpose of the Study:

    • To address the need for a balanced approach to RCIM.
    • To introduce multitask optimization for competitive network seed determination.
    • To develop an effective algorithm for achieving equilibrium across competing diffusion groups.

    Main Methods:

    • A multitask optimization framework was designed to model distinct diffusion scenarios for multiple groups and the overall network.
    • The Multi-Factorial Evolutionary Algorithm for RCIM (MFEA-RCIM) was developed.
    • MFEA-RCIM employs specialized operators for task parallelism and a transfer operation to manage inter-group competition.

    Main Results:

    • MFEA-RCIM demonstrated superior performance compared to existing methods on both synthetic and real-world networks.
    • The multitasking optimization strategy significantly contributed to the algorithm's efficiency.
    • The algorithm successfully achieved a balance across competing diffusion groups.

    Conclusions:

    • Multitask optimization provides a robust framework for tackling the RCIM problem.
    • MFEA-RCIM offers an efficient and effective solution for seed selection in competitive network environments.
    • This work advances the understanding and practical application of influence maximization in complex networks.