Opinion Dynamics & Synchronization in Higher-Order Networks: Using the Kuramoto Model and Simplicial Complexes

Understanding opinion dynamics within complex networks is essential for comprehending collective decision-making processes. Traditional models often fail to account for intricate relationships inherent in complex networks, such as higher-order interactions. Numerous models have been developed historically to analyze the dynamics of opinions. The proposed work introduces a novel approach to study opinion dynamics using the Kuramoto model. Moreover, it investigates how network topology and the inter-node connections influence opinion synchronization by employing simplicial complexes to describe higher-order interactions and expand the Kuramoto model to accommodate these structures. The validity of the proposed framework is demonstrated through the study of real-world data extracted from the social network. In particular, this research enhances our understanding of how opinions propagate in complex networks, offering insights that can inform strategies for consensus-building, opinion manipulation, and influence maximization.