Statistical Methods for Analyzing the Evolution of Social Networks

Abstract

Social networks play a pivotal role in shaping human behavior, decision-making, and the spread of information. This research explores the use of statistical approaches to analyze and model the dynamic nature of social networks. Key methodologies include stochastic modeling, graph theory, and machine learning algorithms tailored to network data. The study focuses on understanding the formation, evolution, and influence of social ties over time, emphasizing the role of homophily, centrality, and community structures. By employing statistical tools like exponential random graph models (ERGMs) and dynamic network analysis (DNA), this research provides insights into how networks evolve and the factors driving changes in connectivity and influence. The findings have practical implications for various domains, including marketing, public health, and social policy, where leveraging network dynamics can optimize interventions and strategies.