Introduction to Social network Analysis and Visualization (3 credits)
Instructor: Muh-Chyun Tang 
muhchyun.tang@gmail.com (TEL) 33662967.
Dept. of Library and Information Science,
National Taiwan University 

Course description

This is an instrouctory course to the basic concepts in social network analysis, with an emphasis on its application in bibiometrics, 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 expereince with SNA techniques, 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 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.  

Course schedule

Week Topic and assignments
 Readings
1
Introduction; the network perspecive  in social sciences and bibliometrics
2 Intro to network data UCINET and NetDraw, Gephi,  datasets Borgatti, S. P., A. Mehr, D. J. Brass, G. Labiance, (2009). Network anaysis in social science
3
 Data collection; two-mode network ego network
How social network predic epidemics
 Borgatti, Everett, & Johnson ( 2018) Ch. 3. Research design, Ch.4. Data collection
4
Graphs Borgatti, Everett, & Johnson ( 2018) Ch. 2 Mathematical foundations
5
 Cohesion, E-I (homophily test)
Clustering coefficient

 Hanneman & Riddle,( 2005) Ch. 7.8

6
 Netowrk centrality and centralization; central-periphery structure/coreness
How trees talk to each other
 First assignment due (data collection and visualization)
Borgatti, Everett, & Johnson ( 2018) Ch. 10. Centrality
7
 Community-detection (clustering)
Girvan Newman Modularity. Louvain method
Borgatti, Everett, & Johnson (2018). Analyzing social networks: Ch. 11 Subgroup, 
8
 Similarity and structural equivalence

 Borgatti, Everett, & Johnson ( 2018) Ch. 12. Equivalence
9
 Social network and Social capital
The hiddern influence of social network
Borgatti, S., Jones, B. C., and Everett, M.G.(1998). Network Measures of Social Capital. Connection 21(2).
10Hypotheis testing with networked data
Multi-plex networks
Second assignment due (Centality; E-I index)
Borgatti, Everett, & Johnson ( 2018) Ch. 8. Testing Hypothesis 
11VosViewer demo
Bibliometrics and network analysis		Giannakos, M., Papamitsiou, Z., Markopoulos, P., Read, J., & Hourcade, J. P. (2020). Mapping childˇVcomputer interaction research through co-word analysis. International Journal of Child-Computer Interaction, 23, 100165.
12
 Bibliometrics and network analysis cont.

13Network filtering procedures
Third assignment due (Community-detection, hypothesis testing)
Borgatti, Everett, & Johnson (2018). Analyzing social networks:
Ch. 14 Large network
14
 Netowrk dynamics: small world, preferential attachment Milgram, Stanley. "The small world problem." Psychology today 2.1 (1967): 60-67
Barabasi (2003) Ch 5, 6, 7
15
 Presentation of your bibliographic network assignment
Forth assignment due (bibliographic networks)
16
 Discussion of your final project
 Aiello, L. M. et al. (2010). Link creation and profile alignment in the aNobii social network
17
 Discussion of your final project
18
 Final presentation

 Final project due
 
Assignments and Grading

I. Participation (10%)
II. Group projects
Students will work in teams to conduct three class assingments and one term project:

1. 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.

2.  Final prject (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

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
Network Repository. An Interactive Scientific Network Data Repository
Stanford large network data collection
Datasets for Gephi
Multiplex network datasets
Marvel universe datasets for Gephi

References

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, 433ˇV443.
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 ˇ§Newˇ¨ Science of Networks. Annual Review of Sociology, 30, 243-270.