課程資訊
課程名稱
社會網絡分析與視覺化
Introduction to Social network Analysis and Visualization 
開課學期
110-1 
授課對象
文學院  圖書資訊學系  
授課教師
唐牧群 
課號
LIS5102 
課程識別碼
126 U1650 
班次
 
學分
3.0 
全/半年
半年 
必/選修
選修 
上課時間
星期四6,7,8(13:20~16:20) 
上課地點
圖資資訊室 
備註
U選課程大學部與研究所學生均可修習。本課程舊名「社會網絡分析專題」,若已有修過該課者請勿再選。
總人數上限:30人 
Ceiba 課程網頁
http://ceiba.ntu.edu.tw/1101LIS5102_ 
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課程概述

This is an instrouctory course to the basic concepts in social network analysis, with an emphasis on its application in bibliometrics, knowledge management and digital humanities. Recent years have witnessed an explosion of interest in social network analysis (SNA). SNA techniques have been applied in a wide range of domains. There has been a close affinity between SNA and bibliometrics in LIS where SNA has been used in the study of scholarly collaboration and citation analysis, as a way of tracing the intellectual influences manifested in collaboration and citation behaviors among scholars. Author collaboration network typology has been used to represent the cohesion of a scholar community, co-word network has been used to reveal the intellectual structure and sub-specialties of a domain. In knowledge management, SNA has also been used to assess the typology of social network in an organization, which has been used to measured the social capital of the individuals as well as the organization as whole. With recent popularity of social networking sites, a growing availability of network data also makes it possible to study similarity and relatedness within a network of people, documents, and websites.

This class is designed for advanced undergraduates or graduate students who wish to acquire a basic understanding of SNA, gain first-hand experience with SNA techniques, and explore the possibility of utilizing SNA for their research. 

課程目標
The class seeks to:
1. provide a survey of the network perspective on a wide range of models and phenomena such as "the small world", "strong/weak ties", and network dynamics such as homophily, reciprocity, and preferential attachment.
2. introduce students to empirical studies utilizing SNA methods in areas such as scholarly communication/bibliometrics, social capital, education, and recommendation networks.
3. give students hand-on experiences with collecting and analyzing network data centered on the software packages UCINET, NetDraw, VosViewer, and Gephi. 
課程要求
待補 
Office Hours
另約時間 
參考書目
Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing social networks. SAGE Publications Limited.
Borgatti, S. P., A. Mehr, D. J. Brass, G. Labiance, (2009). Network Analysis in the Social Sciences. Science (323), p. 892-895.
Burt, R.S. (2005). Brokerage and Closure: an introduction to social capital. Oxford. Burt, R. S. (2000). The Network Structure of Social Capital. Research in Organizational Behavior, 22, 345-423.
Barabasi, A. L. (2003) . Linked: How Everything Is Connected to Everything Else and What It Means. New York: Plume.
Centola, D. (2010). The spread of behavior in an online social network experiment. science, 329(5996), 1194-1197.
Centola, D. (2018). How behavior spreads: The science of complex contagions (Vol. 3). Princeton, NJ: Princeton University Press.
Centola, D. (2010). The spread of behavior in an online social network experiment. science, 329(5996), 1194-1197.
Christakis, N. A. (2010). Connected: Amazing Power Of Social Networks and How They Shape Our Lives. UK: HarperCollins.
Easley, D. & Kleinberg, J. (2010). Networks, Crowds, and Markets: Reasoning About a Highly Connected World. UK:Cambridge University Press.
Golbeck, J. (2013). Analyzing the social web. Newnes.
Hanneman, R. A. & Riddle, M. (2005). Introduction to social network methods. CA: University of California. (at http://faculty.ucr.edu/~hanneman/)
GlU+E4nzel, W., & Schubert, A. (2005). Analysing scientific networks through co-authorship. In Handbook of quantitative science and technology research (pp. 257-276). Springer Netherlands.
McCain, K. W. (1990). Mapping authors in intellectual space: A technical overview. Journal of the American Society for Information Science, 41, 433–443.
Milgram, Stanley. "The small world problem." Psychology today 2.1 (1967): 60-67.
Sandstrom, P.E. (2001). Scholarly communication as a socioecological system. Scientometrics, 51(3), 573-605.
Borgatti, S. P., & Everett, M. G. (1992). Notions of Position in Social Network Analysis. Sociological Methodology, 22, 1-35.
Gilbert, E. & Karahalios, K. (2009). Predicting tie strength with social media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 211-220).
Klavans, R., & Boyack, K. W. (2006). Identifying a better measure of relatedness for mapping science. Journal of the American Society for Information Science and Technology, 57(2), 251-263.
Kadushin, C. (2012). Understanding social networks: Theories, concepts, and findings. Oxford University Press.
Marsden, P. V. (1990). Network Data and Measurement. Annual Review of Sociology, 16, 435-463.
Moody, J. (2004). The structure of a social science collaboration network: Disciplinary cohesion from 1963 to 1999. American sociological review, 69(2), 213-238.
Rafols, I., & Meyer, M. (2010). Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience. Scientometrics, 82(2), 263-287.
Scott, J., & Carrington, P. J. (Eds.). (2011). The SAGE handbook of social network analysis. SAGE publications.
Mislove, A., M. Marcon, K. Gummadi, P. Druschel, and B. Bhattacharjee. Measurement and analysis of online social networks. In IMC, 2007.
Watts, D. J. (2004). Six degrees: The science of a connected age. WW Norton & Company.
Watts, D. J. (2004). The “New” Science of Networks. Annual Review of Sociology, 30, 243-270. 
指定閱讀
待補 
評量方式
(僅供參考)
 
No.
項目
百分比
說明
1. 
Participation 
10% 
 
2. 
Class assignments 
60% 
All group will complete and turn in four class assignments over the course of the semester. These assignments are designed to give you hand-on experiences with collecting, inputting and analyzing network data. 
3. 
Final profect 
30% 
Each group will propose and turn in an empirical research using social network analysis for the final project. The analyse will be driven by research questions or hypotheses (1 to 3) developed by each group. You are to perform various visualization and network analytical techniques we have covered in the classes, including cohesion, centrality, community-detection, and hypothesis-testing. The final project includes also a PowerPoint presentation of your results. Your final project will contain the following components: a. Theoretical framework and research questions (1-2 pages) b. Research procedures (data collection procedures, measures and analytical techniques) (1-5 pages) c. Initial results and discussion d. PowerPoint presentation of your project 
 
課程進度
週次
日期
單元主題
第1週
9/23  Introduction; the network perspective in social sciences and bibliometrics 
第2週
9/30  Intro to network data UCINET and NetDraw, Gephi, datasets 
第3週
10/07  Relational data 
第4週
10/14  Two-modes networks 
第5週
10/21  Graphs 
第6週
10/28  Cohesion, clustering coefficient; E-I (homophily test) 
第7週
11/04  Network centrality and centralization; central-periphery structure/coreness 
第8週
11/11  Network Modeling: Random network; Small world; Traid closure; Preferential attachment; Reciprocity 
第9週
11/18  Social network and Social capital 
第10週
11/25  Community-Detection 
第11週
12/02  VosViewer; bibliographic network; filtering 
第12週
12/09  Structural equivalent and clustering 
第13週
12/16  Statistical testing 
第14週
12/23  Dynamic network; discussion of your final project 
第15週
12/30  Discussion of your final project 
第16週
1/06  Final presentation