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    This study introduces a heterogeneous opinion dynamics model for interdependent topics in dynamic networks. Stubborn individuals significantly influence opinion evolution and consensus in complex, multi-topic environments.

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    Area of Science:

    • Social Network Analysis
    • Computational Social Science
    • Opinion Dynamics

    Background:

    • Understanding opinion formation in complex social networks is crucial.
    • Existing models often simplify topic interdependencies and network dynamics.
    • The influence of persistent dissenting opinions (stubborn individuals) requires further investigation.

    Purpose of the Study:

    • To propose a heterogeneous opinion dynamics model for multiple interdependent topics.
    • To analyze consensus and convergence in a time-varying network with competing relationships.
    • To investigate the impact of stubborn individuals on opinion evolution.

    Main Methods:

    • Development of a heterogeneous opinion dynamics model.
    • Utilization of a reducible topic matrix to represent topic interdependencies.
    • Application of a topic partition method for multi-topic analysis.
    • Simulations to observe opinion evolution with varying numbers of stubborn individuals.

    Main Results:

    • The proposed model effectively captures opinion dynamics across interdependent topics.
    • The number of stubborn individuals demonstrably affects opinion convergence and consensus.
    • Network topology and topic interdependencies play significant roles in opinion spread.

    Conclusions:

    • Stubborn individuals are key factors in shaping opinion dynamics within complex topic networks.
    • The reducible topic matrix provides a robust framework for modeling topic interdependencies.
    • The findings offer insights into opinion formation and consensus-building in dynamic social systems.