課程名稱 |
網絡資料分析與模式 Network Data Analysis and Models |
開課學期 |
109-2 |
授課對象 |
共同教育中心 統計碩士學位學程 |
授課教師 |
溫在弘 |
課號 |
Geog5076 |
課程識別碼 |
228 U2880 |
班次 |
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學分 |
3.0 |
全/半年 |
半年 |
必/選修 |
選修 |
上課時間 |
星期五2,3,4(9:10~12:10) |
上課地點 |
地理一教室 |
備註 |
本課程中文授課,使用英文教科書。工程與環境統計領域選修課程之一。 總人數上限:30人 |
Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/1092_Geog5076 |
課程簡介影片 |
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核心能力關聯 |
本課程尚未建立核心能力關連 |
課程大綱
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課程概述 |
This course explores quantitatively the concepts and applications of network data analysis and how these theories assist scientists in understanding the complex patterns of connections and relationships that shape our lives. Students will acquire the ability to use the modeling and statistical methods to analyze link-node structures and behaviors of a complex network. They will also explore some emerging topics on human behavior analysis, social interactions and peer influence, disease transmission, innovation diffusion, and spatial structures of migration. |
課程目標 |
The objectives of the course are to guide students on how to describe and characterize quantitatively various types of network topology and the use of appropriate analytical methods and statistical models to explain the features of complex networks and mechanisms of the social-spatial process. |
課程要求 |
Course participation, weekly readings, and written assignments |
預期每週課後學習時數 |
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Office Hours |
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指定閱讀 |
Valente (2010). Social Networks and Health: Models, Methods, and Applications. New York: Oxford University Press.
Kolaczyk, Eric D., Csardi, Gabor (2020), Statistical Analysis of Network Data with R, New York: Springer-Verlag.
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參考書目 |
Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2013). Analyzing Social
Networks. SAGE Publications. ISBN: 978-1446247419.
Newman (2010). Networks: An Introduction. New York: Oxford University Press
Menczer, Fortunato, Davis (2020), A First Course in Network Science, New York:
Cambridge University Press. ISBN: 978-1108471138. |
評量方式 (僅供參考) |
No. |
項目 |
百分比 |
說明 |
1. |
Mid term exam |
30% |
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2. |
Final exam |
30% |
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3. |
Weekly written assignments and discussion in class |
40% |
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週次 |
日期 |
單元主題 |
第1週 |
2/26 |
Course Introduction: Networks in Social and Geographical Spaces |
第2週 |
3/05 |
Networks as Social Relationships: An Introduction |
第3週 |
3/12 |
Network Data Collection and Visualization |
第4週 |
3/19 |
Measures for Ego-Networks: Structure Hole |
第5週 |
3/26 |
Network Measures: Centrality (Individual-level) |
第6週 |
4/02 |
* Spring break * |
第7週 |
4/09 |
Network Measures: Groups and Community Structures |
第8週 |
4/16 |
Network Measures: Positions and Structural Equivalence |
第9週 |
4/23 |
* Mid-term Exam * |
第10週 |
4/30 |
Lab: Creating R Shiny Dashboard |
第11週 |
5/07 |
Network-Level Measures + Two-mode Networks |
第12週 |
5/14 |
Network Autocorrelation Models |
第13週 |
5/21 |
Spatial Networks: Linking Geospatial Data |
第14週 |
5/28 |
Predicting with Networks: QAP Regression Models |
第15週 |
6/04 |
Exponential Random Graph Models (ERGM) |
第16週 |
6/11 |
Temporal Network Dynamics |
第17週 |
6/18 |
* Break * |
第18週 |
6/25 |
Final Report Submission |
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