Course Information
Course title
Economic Analysis of Social Networks 
Semester
109-2 
Designated for
COLLEGE OF SOCIAL SCIENCES  GRADUATE INSTITUTE OF ECONOMICS  
Instructor
CHIH-SHENG HSIEH 
Curriculum Number
ECON7217 
Curriculum Identity Number
323EM3760 
Class
 
Credits
3.0 
Full/Half
Yr.
Half 
Required/
Elective
Elective 
Time
Tuesday 2,3,4(9:10~12:10) 
Remarks
Restriction: MA students and beyond OR Restriction: Ph. D students
The upper limit of the number of students: 25. 
 
Course introduction video
 
Table of Core Capabilities and Curriculum Planning
Table of Core Capabilities and Curriculum Planning
Course Syllabus
Please respect the intellectual property rights of others and do not copy any of the course information without permission
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.待補 

Course Objective
After completing this course, students should be
1. acquainted with basic terminologies in social and economic network analysis.
2. able to perform econometric regressions on network data and provide economic interpretations.
3. able to use software to arrange data and conduct network analysis. 
Course Requirement
1. Course participation 20%
There will be several on-lecture assignments which exercise the use of R

2. Course presentation 40%
Students should present one article from the reference list

3. Research proposal 40%
Students have to hand in one research proposal at the end of the semester. Students should meet and discuss their proposals with the instructor at least once before submitting the proposal. 
Student Workload (expected study time outside of class per week)
 
Office Hours
Mon. 13:30~15:00 Note: or by appointment 
References
 
Designated reading
 
Grading
   
Progress
Week
Date
Topic
No data