A Comparative Analysis of Social Network Epidemic Models

In recent years, the ability to predict the propagation of information within networks has become increasingly crucial, particularly in the context of online social media platforms where the dissemination of information can significantly impact social events. Researchers employ diverse methods, including network mapping and analysis, data mining, and machine learning algorithms, to investigate social networks and their connections. These methods help identify key nodes and influential elements within a network and comprehend the underlying dynamics governing knowledge dissemination. This research focuses on applying three epidemic models, namely SI, SIS, and SIR, to social network datasets at varying levels of complexity, ranging from small to medium and complex networks. The paper delves into the impact of centrality measures such as degree, betweenness and eigen vector and presents the comparison of the models. It concludes by recommending the most suitable social network epidemic model and the centrality measure for enhancing information diffusion within the network, offering potential applications for businesses in digital marketing and advertising.