課程資訊
課程名稱
社會網絡分析專題
Special Topic on Social Network Analysis 
開課學期
108-2 
授課對象
學程  知識管理學程  
授課教師
唐牧群 
課號
LIS5070 
課程識別碼
126 U1390 
班次
 
學分
2.0 
全/半年
半年 
必/選修
選修 
上課時間
星期一3,4(10:20~12:10) 
上課地點
圖資資訊室 
備註
KM學程管理領域選修課程。
總人數上限:30人 
Ceiba 課程網頁
http://ceiba.ntu.edu.tw/1082LIS5070_ 
課程簡介影片
 
核心能力關聯
核心能力與課程規劃關聯圖
課程大綱
為確保您我的權利,請尊重智慧財產權及不得非法影印
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 and knowledge management. 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 applied in the study of scholalry collaboration and citation analysis, as a way of tracing the intellectual influences manifested in collaboratiion and citation behaviors among scholars. In knowledge management, SNA has also been used to assess the structure component of social capital, which explains the patterns of information exchange and team performance within an organization. With the 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. 

課程目標
This class is designed for advanced undergraduates or graduate students who wish to acquire a basic understanding of SNA and explore the possibility of utilizing SNA for their research. The class seeks to:
1. provide a survey of the network perspective on a wide range of theories and phenomena such as "the small world", "strong/weak ties", and power law, with a specific focus on their implications on social and behavioral sciences.
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
Assignments and Grading

I. Participation (10%)
II. Group projects
Students will form into groups of two to three to complete the following group project.
1. Class assignments (40%)
All Students will be given three class exercises in the semester. These assignments are designed to give you hand-on experiences with collecting, inputting and analyzing network data. You will be asked to work with two datasets upon which you are to perform various SNA methods and from which you will also generate and test your own hypotheses.

2. Empirical study review (20%)
Each group is required to choose and give a 20 minutes power point presentation of a SNA related empirical study. You can find the list of "review articles " in the reference list. The date for each of the reviewed articles has been specified in the "Course Schedule" so in choosing the article you want to review you are also determining when you will do the presentation.
No written report for this assignment. Prepare a 20 minutes power point presentation and a 5-10 minutes Q&A session. The power point file is to be posted on the class website one day before the date on which your presentation is scheduled.

3. Final prject (30%)
Students can opt for either an empirical research project or a research proposal.
For the empirical research, students will conduct a series of social network analyses on the readily available datasets circulated in the class. The analyese will be driven by research questions or hypotheses (2 to 5) developed within each group. The final project will be in the format of a 20-30 powerpoint presentation at the end of the semester. 
預期每週課後學習時數
 
Office Hours
 
參考書目
SNA resources and data online
A very user friendly instroduction to network theory
Demo Gephi Citation Network Analysis with Scopus Data
UCI network data repository
Stanford large network data collection
Datasets for Gephi
Marvel universe datasets for Gephi

References
Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2013). 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.
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.
Fisher, D., Smith, M., & Welser, H. T. (2006, January). You are who you talk to: Detecting roles in usenet newsgroups. In System Sciences, 2006. HICSS'06. Proceedings of the 39th Annual Hawaii International Conference on (Vol. 3, pp. 59b-59b). IEEE
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/)
Glanzel, 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.
Reagans, R., & Zuckerman, E. W. (2001). Networks, diversity, and productivity: The social capital of corporate R&D teams. Organization science, 12(4), 502-517.
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). 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)
   
課程進度
週次
日期
單元主題
第4週
3/23  data input