Applications and Future Directions of Social Network Analysis and Related Models

Abstract

: The rapid development of the Internet improves the application of complex network structures in different important areas, including social media, healthcare, and transportation. As a methodological framework, social network analysis supports a powerful view, for learning the interaction and its influence among individuals in connected systems. This article reviews basic principles and theoretical foundations of this scientific region, and introduces graph theory and its related statistical models such as exponential random graph models and stochastic actor-based models. Based on these models, researchers can describe the dynamic social relationships, developed network evolution, and information transmissions. Social network analysis is also important to improve information sharing in social media and healthcare systems, meanwhile identifying key nodes that assist in transportation or communication networks. Analytical capacity of this analysing method is expected to be more precise by developing big data and models continuously, leading to more accurate predictions and dynamic insights about increasingly complex social networks.