Applications of social network analysis in data science

Abstract

In an era dominated by digital connectivity, Social Network Analysis (SNA) plays a critical role in data science, enabling the analysis of relationships, behaviors, and influence patterns within social networks. This paper explores key applications of SNA across different fields, including marketing, education, security, and misinformation detection. By leveraging network-based metrics and algorithms, SNA provides powerful insights into data-driven decision-making. The paper also discusses ethical concerns related to data privacy and regulatory compliance. The case study on misinformation spread detection further demonstrates how SNA can be used to counter false narratives in online environments. The findings highlight the importance of integrating computational techniques with network science to enhance data interpretation and application. Additionally, challenges such as data privacy, computational complexity, and ethical considerations in SNA implementation are addressed.