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Application of Markov Chain Monte Carlo Methods in Social Network Theory Models: A Social Capital Perspective

Abstract

This study examines the use of the Markov Chain Monte Carlo (MCMC) method in social network theory models, focusing on social capital. The research revisits core concepts of social capital theory and social network analysis, detailing MCMC's application in social network simulation, dynamic modeling of social capital, and parameter inference. The findings demonstrate that MCMC effectively addresses high-dimensional issues and dynamic processes in complex social networks, offering a new analytical tool for social capital research. This study introduces a framework integrating social capital theory, social network analysis, and statistical physics, enhancing the understanding of social capital dynamics and providing methodological guidance for future studies.

Keywords

Social capitalsocial networkMCMCcomplex system modeling

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