Course Information
Course title
Seminar on Network Governance 
Designated for
Chien-shih Huang 
Curriculum Number
Curriculum Identity Number
Monday 3,4(10:20~12:10) 
Restriction: juniors and beyond
The upper limit of the number of students: 20.
The upper limit of the number of non-majors: 10. 
Course introduction video
Table of Core Capabilities and Curriculum Planning
Table of Core Capabilities and Curriculum Planning
Course Syllabus
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Course Description

Social network analysis is the broad set of theories and statistical approaches aimed at understanding the interdependence between units, words, people, organizations, or countries. This course provides an introduction to the use of social network analysis for the study of network governance on various policy issues. Students will be more familiar with network concepts, measurements and the related theory. The course will also help students understand the methodological underpinning of this approach and provide examples of substantive applications. Students are then capable to run social network analysis in the open-source computational environment as well as interpret and explain the observed inter-dependency in public policy and governance. 

Course Objective
By the end of the course, graduate students should be able to:
1. Ask questions to which social network analysis can be applied.
2. Describe and compare individuals’ positions within a network.
3. Describe and compare the features of networks.
4. Measure relationships between network features, network positions, and individual attributes.
5. Use appropriate social network analytical tools to illustrate and explain how social connections can be build in the field of public policy and management.
6. Develop and test theories that account for the interdependence between actors. 
Course Requirement
Students are required to read the weekly readings and participate in in-class discussion. They also have to complete five homework assignments by using statistical analytical tools they lean in class. Finally, they will need to ask research questions relevant to network governance, apply social network analysis and write up a research paper.
Student Workload (expected study time outside of class per week)
Office Hours
Mon. 13:00~17:00 Note: Students can meet with the instructor by appointment if needed  

1. Lusher, Dean, Johan Koskinen, & Garry Robins. (ed.) (2013) Exponential Random Graph Models for Social Network. Cambridge University Press.
2. Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing Social Networks. Sage Publisher.
3. Perry, B., Pescosolldo, B., & Borgatti, S. P. (2018). Egocentric Network Analysis: Foundation, Methods, and Models. Cambridge University Press.
4. Cranmer, S. J., Desmarais, B. A., & Morgan, J. W. (2020). Inferential Network Analysis. Cambridge University Press.
5. Light, R., & Moody, J. (Eds.). (2020). The Oxford Handbook of Social Networks. Oxford University Press. 
Designated reading
Required Text: Wasserman, Stanley and Katherine L. Faust. (1994). Social Network Analysis: Methods and Applications. New York: Cambridge University Press.

Other readings: A number of articles and book chapters will be used and be made available by the instructor online. 
Explanations for the conditions
Students are required to read the weekly readings and participate in in-class discussion/exercises. Students will be able to see their participation grade for every class on NTU Cool; grades will be posted by the end of the week. Two lower participation grade will be dropped. The grading criteria is: – 2/2 points per week– the student participates in class by providing at least two insightful opinions, and completes the in-class exercises, if any exercise is given. – 1/2 points per week– the student participates in class but fails to providing at least two insightful opinions, or does not completes the in-class exercises, if any exercise is given. – 0/2 points per week– the student does not show up to the class 
Students will complete five assignments based on the course material. Specific instruction for each assignment will be provided the week before it is due. All assignments are due by 6 pm on the Saturday before the relevant class and must be submitted to NTU Cool by email in a single pdf document. The document title must use the "NetGov- lastname-A#" format (e.g., my assignment 2 file would be named "NetGov-Huang-A2"). Late assignment will not be accepted except with a documented excuse. All assignments must be carefully proofread and properly formatted using the APA guidelines (https:// general_format.html), single spaced, with 12-point Times New Roman font, and one-inch margins. All graphics and tables should be approaching publication quality. Assignments must include all relevant R code used to perform the analysis. All written discussion of statistics should strive to provide clear and accurate interpretations that a social scientist with no networks training could understand. 
Final paper presentation 
In the last meeting of the course, each student will present their final paper. Presentations should be 10-15 minutes and must include slides. 
Research Paper 
The final assignment in this course is to write a paper presenting original empirical analysis of social network data. The research paper should follow the APA guidelines. Plagiarism will directly result in failure of this course. No exceptions. 
Week 1
Feb. 14th  Course Introduction 
Week 2
Feb. 21st   Why Social Network Analysis? 
Week 3
Feb. 28th  National Holiday 
Week 4
March 7th  Collect Network Data 
Week 5
March 14th  Describing Networks 
Week 6
March 21st  Egocentric and Whole Networks 
Week 7
March 28th  Network Subgroups and Visualization 
Week 8
April 4th  National Holiday 
Week 9
April 11th  Small Worlds 
Week 10
April 18th  Tie Strength 
Week 11
April 25th  Homophily and Balance 
Week 12
May 2nd  Position and Structure 
Week 13
May 9th  Inferential Network Analysis (I): QAP/Conditional Uniform Graph 
Week 14
May 16th  Inferential Network Analysis (II) : Exponential random graph model (I) 
Week 15
May 23th  Inferential Network Analysis (III) : Exponential random graph model (II) 
Week 16
May 30th  Research presentation by students