In this course, we will introduce economic analysis on social networks. Reflected by the rapid growing number of network studies in different economic fields, such as in labor, health, development, international, and financial economics, social (or economic) network has become an attractive and must-know subject for graduate students in economics, particularly in this big data era when network data become widely available.
We will begin this course by discussing the characterization of networks. Then we will visit some representative empirical studies which perform regressions based on network data. Next, we will discuss various kinds of statistical approaches for analyzing network data, including network sampling, community (cluster) detection, modelling network (spillover) effects, network formation, and relevant policy implications, etc.
Throughout this course, students do not only learn statistical models for networks, but also learn how to use the statistical software R to collect, arrange, and analyze network data. Students will also learn software such as Gephi to facilitate visualization of network graphs.待補