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Course title |
Economic Analysis of Social Networks |
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Semester |
110-2 |
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Designated for |
COLLEGE OF SOCIAL SCIENCES GRADUATE INSTITUTE OF ECONOMICS |
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Instructor |
CHIH-SHENG HSIEH |
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Curriculum Number |
ECON7217 |
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Curriculum Identity Number |
323EM3760 |
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Class |
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Credits |
3.0 |
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Full/Half
Yr. |
Half |
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Required/
Elective |
Elective |
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Time |
Tuesday 2,3,4(9:10~12:10) |
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Remarks |
Restriction: MA students and beyond OR Restriction: Ph. D students
The upper limit of the number of students: 25. |
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Course introduction video |
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Table of Core Capabilities and Curriculum Planning |
Table of Core Capabilities and Curriculum Planning |
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Course Syllabus
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Please respect the intellectual property rights of others and do not copy any of the course information without permission
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Course Description |
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. |
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Course Objective |
After completing this course, students should be:
1. Acquainted with basic terminologies in social and economic network analysis.
2. Familiar with theoretical development on 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. |
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Course Requirement |
待補 |
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Student Workload (expected study time outside of class per week) |
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Office Hours |
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Designated reading |
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References |
1. Blume, L. E., Brock, W. A., Durlauf, S. N., & Ioannides, Y. M. (2011). Identification of social interactions. In Handbook of Social Economics (Vol. 1, pp. 853-964). North-Holland.
2. Borgatti, S. P., Everett, M. G., and Johnson, J. C. Analyzing Social Networks, Sage, 2013
3. Bramoulle, Yann, Andrea Galeotti, and Brian Rogers. The Oxford Handbook of the Economics of Networks. Oxford University Press, 2016.
4. De Paula (2017). Econometrics of Network Models, In B. Honore, A. Pakes, M. Piazzesi and L. Samuelson (Eds.), Advances in Economics and Econometrics: Theory and Applications: Eleventh World Congress (Econometric Society Monographs, pp.268-323), Cambridge: Cambridge University Press
5. Goldenberg, A., Zheng, A. X., Fienberg, S. E., & Airoldi, E. M. (2010). A survey of statistical network models. Foundations and Trends簧 in Machine Learning, 2(2), 129-233.
6. Hsieh, C.S., Lin, X., and Patacchini, E. (2019). Social Interaction Methods. To be appeared in Handbook of Labor, Human Resources and Population Economics.
7. Matthew, Jackson. Social and Economic Networks, Princeton University Press, 2008.
8. Matthew, Jackson and Zenou, Yves. Games on Networks, Handbook of Game Theory, Vol. 4, Amsterdam: Elsevier, 2014.
9. Kolaczyk, E. D., Statistical Analysis of Network Data: Method and Models, Springer, 2009.
10. Kolaczyk, E. D. and Csardi, G., Statistical Analysis of Network Data with R, Springer, 2014.
11. Newman, M. (2010) Networks: An Introduction, Oxford University Press |
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Grading |
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