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課程名稱 |
網絡治理專題
Seminar on Network Governance |
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開課學期 |
110-2 |
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授課對象 |
社會科學院 國家發展研究所 |
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授課教師 |
黃建實 |
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課號 |
NtlDev5322 |
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課程識別碼 |
341EU9360 |
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班次 |
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學分 |
2.0 |
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全/半年 |
半年 |
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必/選修 |
選修 |
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上課時間 |
星期一3,4(10:20~12:10) |
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上課地點 |
國發206 |
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備註 |
本課程以英語授課。
限學士班三年級以上
總人數上限：20人
外系人數限制：10人 |
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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
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課程大綱
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課程概述 |
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. |
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課程目標 |
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 built in the field of public policy and management.
6. Develop and test theories that account for the interdependence between actors. |
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課程要求 |
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.
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預期每週課後學習時數 |
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Office Hours |
每週一 13:00~17:00 備註： Students can meet with the instructor by appointment if needed |
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指定閱讀 |
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. |
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參考書目 |
Lusher, D., Koskinen, J., & Robins, G. (Eds.). (2013). Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications. Cambridge University Press.
Bianconi, G. (2018). Multilayer Networks: Structure and Function. Oxford university press.
Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing Social Networks. Sage
Publisher.
Perry, B., Pescosolldo, B., & Borgatti, S. P. (2018). Egocentric Network Analysis:
Foundation, Methods, and Models. Cambridge University Press.
Cranmer, S. J., Desmarais, B. A., & Morgan, J. W. (2020). Inferential Network Analysis.
Cambridge University Press.
Light, R., & Moody, J. (Eds.). (2020). The Oxford Handbook of Social Networks. Oxford
University Press.
Stokman, F. N., & Doreian, P. (2013). Evolution of Social Networks. Routledge. |
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評量方式
(僅供參考) |
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No. |
項目 |
百分比 |
說明 |
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1. |
Participation |
20% |
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 |
2. |
Homework |
50% |
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 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://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/ 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. |
3. |
Final paper presentation |
10% |
In the last meeting of the course, each student will present their final paper. Presentations should be 10-15 minutes and must include slides. |
4. |
Research Paper |
20% |
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. |
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週次 |
日期 |
單元主題 |
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第1週 |
2/14 |
Course introduction |
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第2週 |
2/21 |
Why social network analysis? |
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第3週 |
2/28 |
National holiday |
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第4週 |
3/07 |
Collect network data |
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第5週 |
3/14 |
Describing Networks |
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第6週 |
3/21 |
Egocentric and Whole Networks |
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第7週 |
3/28 |
Network Subgroups and Visualization |
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第8週 |
4/04 |
National holiday |
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第9週 |
4/11 |
Small Worlds |
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第10週 |
4/18 |
Tie Strength |
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第11週 |
4/25 |
Homophily and Balance |
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第12週 |
5/02 |
Position and Structure |
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第13週 |
5/09 |
Inferential Network Analysis (I): QAP/Conditional Uniform Graph |
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第14週 |
5/16 |
Inferential Network Analysis (II) : Exponential random graph model (I) |
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第15週 |
5/23 |
Inferential Network Analysis (III) : Exponential random graph model (II) |
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第16週 |
5/30 |
Research Presentation by Students |
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