Enhancing the Kuramoto model for modeling information dissemination in social networks

DOI: 10.31673/2412-4338.2024.048612

Abstract

The article explores enhancements to the Kuramoto model for analyzing information dissemination in social networks by integrating additional parameters and modifications. Originally designed for studying synchronization in physical and biological systems, the Kuramoto model has proven its effectiveness in social sciences, particularly for modeling collective user behavior in social networks. The paper introduces several improved versions of the Kuramoto model. First, integrating principles from the cascade model enables the consideration of the probability of information transfer between users, significantly increasing the accuracy of representing real processes. This enhancement allows for forecasting peak moments of content dissemination and its viral potential, which is particularly beneficial for optimizing marketing campaigns. Second, adapting the Kuramoto model using the epidemic model facilitates modeling the dynamics of information spread, accounting for users transitioning between states: susceptible, infected, and recovered. This allows for the modeling of recurring waves of information popularity, analyzing its decline, and planning long-term information campaigns. The third improvement is based on the application of the rumor spreading model, which accounts for changes in user behavior influenced by social connections and trust levels. This modification provides more accurate modeling of information flows in social networks, aiding in forecasting viral content and combating misinformation. The fourth enhancement involves accounting for the impact of key network nodes through the integration of the influential user model. This approach enables modeling the effect of opinion leaders on system synchronization, improving the prediction of content dissemination and the efficiency of information campaigns in social networks. Comparative studies between the baseline and enhanced models demonstrate the significant advantage of the latter in achieving synchronization among network nodes, which is particularly critical for the rapid dissemination of information in large networks. Graphs presented in the article visually illustrate the effectiveness of these modifications. Future research proposes expanding the Kuramoto model by incorporating factors such as emotions, social status, and dynamic changes in connections. An important task is verifying the results on large datasets from social networks and optimizing computational algorithms for the model's application in large-scale networks. These developments open prospects for creating effective tools for forecasting viral content, managing information flows, and combating misinformation. Thus, the article offers a new perspective on modeling social processes, demonstrating the universality and effectiveness of the Kuramoto model for analyzing information dissemination in complex networks.

Keywords: Kuramoto model, synchronization, information dissemination, cascade model, epidemic model, rumor spreading model, influential user model.

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