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    This summary is machine-generated.

    This study introduces a new framework for optimizing viral marketing in geo-social networks. It balances maximizing influence spread with minimizing promotion costs using multiobjective optimization.

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    Area of Science:

    • Computer Science
    • Social Network Analysis
    • Marketing Science

    Background:

    • Geo-social networks are emerging systems for viral marketing and targeted advertising.
    • Traditional influence spread models do not account for heterogeneous spatial distributions.
    • Balancing influence maximization and promotion cost minimization is crucial for businesses.

    Purpose of the Study:

    • To develop a multiobjective optimization framework for influence spread in geo-social networks.
    • To reveal Pareto-optimal solutions for decision-makers balancing competing objectives.
    • To address the challenge of heterogeneous spatial distribution in influence spread.

    Main Methods:

    • A similarity matching-based reverse influence sampling (RIS) method is proposed to handle diverse users.
    • The problem is transformed into a weighted coverage problem.
    • A greedy-based incremental approximation approach and a heuristic-based particle swarm optimization approach are developed.

    Main Results:

    • The proposed framework effectively reveals Pareto-optimal solutions for decision-making.
    • The similarity matching-based RIS method accommodates diverse user characteristics.
    • Experimental validation on real-world geo-social networks demonstrates the effectiveness and efficiency of the proposed approaches.

    Conclusions:

    • The developed framework provides a comprehensive approach to influence spread optimization in geo-social networks.
    • The proposed methods offer efficient solutions for balancing marketing objectives.
    • This research contributes to the strategic application of geo-social networks in viral marketing.