Type of Credit: Elective
Credit(s)
Number of Students
This course is interdisciplinary, covering the mathematical foundations of social network analysis (SNA), including SNA data collection and data analysis processes. Various SNA methods will be introduced, and then students will be asked to practice them using R. Lecture slides and open-source software practices will be provided, and active participation in various activities is expected throughout the course. Students who enjoy interdisciplinary learning processes are encouraged to enroll. Active participation and frequent inquiries during classes are also expected. Toward the end of the course, students will have the option to apply the knowledge gained to complete individual/group projects. The course aims to provide a high level of learning achievement, allowing students to find satisfaction in their academic progress.
能力項目說明
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
|
週次Week |
課程主題 |
課程內容與指定閱讀 |
教學活動與作業Teaching Activities and Homework |
學習投入時間 Student workload expectation |
|
|
課堂 In-class Hours |
課程 Outside-of -class Hours |
||||
|
W1 - Feb 21 |
Introduction |
Syllabus Review / Intro to textbook and references / Mathematical Foundations |
[ASNR Ch1-2] |
3 |
4.5 |
|
W2 - Feb 28 |
[NO CLASS] |
Online Resources |
0 |
9 |
|
|
W3 - Mar 06 |
Data for Research |
Research Design |
[ASNR Ch3] |
3 |
4.5 |
|
W4 - Mar 13 |
Data Collection |
[ASNR Ch4] |
3 |
4.5 |
|
|
W5 - Mar 20 |
Data Management |
[ASNR Ch5] |
3 |
4.5 |
|
|
W6 - Mar 27 |
Data Analysis Foundations |
Multivariate Techniques Used in Network Analysis |
[ASNR Ch6] |
3 |
4.5 |
|
W7 - Apr 03 |
[NO CLASS] |
Ind. Practices 1/2 |
0 |
9 |
|
|
W8 - Apr 10 |
Visualization |
[ASNR Ch7] |
3 |
4.5 |
|
|
W9 - Apr 17 |
Local Node-level Measures |
[ASNR Ch8] |
3 |
4.5 |
|
|
W10 - Apr 24 |
Centrality |
[ASNR Ch9] |
3 |
4.5 |
|
|
W11 - May 01 |
Group-level Measures |
[ASNR Ch10] |
3 |
4.5 |
|
|
W12 - May 08 |
Subgroups and Community Detection |
[ASNR Ch11] |
3 |
4.5 |
|
|
W13 - May 15 |
Equivalence |
[ASNR Ch12] |
3 |
4.5 |
|
|
W14 - May 22 |
Analyzing Two-mode Data |
[ASNR Ch13] |
3 |
4.5 |
|
|
W15 - May 29 |
Data Analysis Advanced |
Introduction to Inferential Statistics for Complete Networks |
[ASNR Ch14] |
3 |
4.5 |
|
W16 -Jun 05 |
ERGMs |
[ASNR Ch15] |
3 |
4.5 |
|
|
W17 - Jun 12 |
SAOMs |
[ASNR Ch15] |
3 |
4.5 |
|
|
W18 - Jun 19 |
SNA Project Demo |
Individual or Group Project Demo - Will be video recorded during the presentation |
Review |
0 |
9 |
50% Quizzes - five times
20% Individual Practices - two times in W7 and W17
30% Individual or Group SNA project demo in W18
You are also totally free to use generative AI ~ :)
Textbook
Borgatti, S. P., Everett, M. G., Johnson, J. C., & Agneessens, F. (2022). Analyzing social networks using R. SAGE Publications Asia-Pacific Pte Ltd. [ASNR]
References
• NCCU Moodle
• UCInet Software https://sites.google.com/site/ucinetsoftware/home
• Steve Borgatti https://sites.google.com/site/steveborgatti/home
• Social Network Analysis by Duke University's Mod-U channel on YouTube
https://www.youtube.com/playlist?list=PL1M5TsfDV6Vs7tnHGNgowEUwJW-O8QVp5
• The Historical Network Research Community https://www.youtube.com/@HistoricalNetworkResearch
• Network Culture https://networkcultures.org/geert/