In this course, I will introduce theoretical and empirical analysis of social networks. Reflected by the rapidly growing number of network studies in different economic fields, such as in labor, health, development, international, and financial economics, social (or economic) networks have now become attractive and must-know subjects for graduate students in economics, particularly when data become widely available in this big data era. We will begin this course by learning the characterization of networks. Then we will visit the theoretical literature on games on networks. Next, we will discuss various empirical approaches for network data, including network sampling, community (cluster) detection, modeling network (spillover) effects, network formation, etc. Before the end of this course, we will discuss policy implications from a few classic network studies.

After completing this course, students should be: 1. Acquainted with basic terminologies in social and economic network analysis. 2. Familiar with the theoretical development of network games 3. Able to perform econometric regressions on network data and provide economic interpretations. 4. Able to use software R to collect data and conduct network analysis.

Bramoulle, Yann, Andrea Galeotti, and Brian Rogers. The Oxford Handbook of the Economics of Networks. Oxford University Press, 2016. Borgatti, S. P., Everett, M. G., and Johnson, J. C. (2013) Analyzing Social Networks, Sage. Jackson, Matthew, Social and Economic Networks, Princeton University Press, 2008. Jackson, Matthew, and Zenou, Yves, Games on Networks, Handbook of Game Theory, Vol. 4, Amsterdam: Elsevier, 2014 Kolaczyk, E. D. (2009) Statistical Analysis of Network Data: Method and Models, Springer Kolaczyk, E. D. and Csardi, G. (2014) Statistical Analysis of Network Data with R, Springer Newman, M. (2010) Networks: An Introduction, Oxford University Press