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 圖書資訊學研究所 |
授課教師 |
唐牧群 |
課號 |
LIS5102 |
課程識別碼 |
126 U1650 |
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_ |
課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
課程大綱
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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 |
預期每週課後學習時數 |
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Office Hours |
另約時間 |
指定閱讀 |
待補 |
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/)
Glänzel, 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. |
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 |
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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 |
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