Introduction of Social Network Analysis


Instructor: Zixi Chen, PhD
Dates: August 2 – August 6, 2024

Course Description

We live in a connected world, both online and offline. Understanding the complex social relationships using social network analysis (SNA) is crucial for social scientists. This course introduces participants to the fundamental concepts of social networks, key measures of network structures, statistical modeling for understanding the selection and influence of networks, and network visualizations. With the burgeoning availability of online trace data with diverse types of data (such as texts and images), this course also extends to introducing applications for understanding emerging online social networks and text networks. The course places a strong emphasis on learning and applying SNA techniques using the R programming language, equipping participants with practical skills for manipulating, analyzing, and visualizing network data. The course will provide opportunities for participants to work on their projects, applying the SNA techniques learned in class to their specific research questions.

About the Instructor

Dr. Zixi Chen is an Assistant Professor of Practice in Computational Social Science at New York University Shanghai. She received her PhD in Measurement and Quantitative Methods from Michigan State University. She also worked as a postdoctoral researcher at the Learning Informatics Lab at the University of Minnesota and as a visiting scholar at Infinite Campus, a leading US EdTech company. Chen’s research broadly focuses on developing and implementing novel computational and statistical methods to investigate educational and socio-cultural behaviors in digital spaces. Her scholarly work has appeared in leading educational journals, such as American Journal of Education, Journal of Research on Technology in Education, and Teachers College Record. Alongside her research, she is passionate about teaching in the realms of computational social science and quantitative methods.

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