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
107-2 
授課對象
文學院  圖書資訊學研究所  
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
星期三6,7(13:20~15:10) 
上課地點
圖資資訊室 
備註
U選課程,學士班與碩士班學生均可選修。
總人數上限:30人 
Ceiba 課程網頁
http://ceiba.ntu.edu.tw/1072LIS5070_ 
課程簡介影片
 
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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 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
 
指定閱讀
待補 
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. 
評量方式
(僅供參考)
   
課程進度
週次
日期
單元主題
第1週
2/20  Course introduction 
第2週
2/27  relational data  
第3週
3/06  graph 
第4週
3/13  cohesion 
第5週
3/20  Intro to gephi 
第6週
3/27  core-periphery
and a little bit of two-mode to one mode network 
第8週
4/10  centrality  
第9週
4/17  Clustering coefficient; small world 
第10週
4/24  strong tie, weak tie 
第11週
5/01  community detection 
第13週
5/15  structural equivalent 
第14週
5/22  filtering and large network 
第15週
5/29  Testing hypothesis 
第16週
6/05  social contagion