Master
2024/2025
Introduction to Network Analysis
Type:
Compulsory course (Data Analytics and Social Statistics)
Area of studies:
Applied Mathematics and Informatics
Delivered by:
International Laboratory for Applied Network Research
Where:
Faculty of Social Sciences
When:
1 year, 2 module
Mode of studies:
distance learning
Online hours:
40
Open to:
students of one campus
Master’s programme:
Аналитика данных и прикладная статистика
Language:
English
ECTS credits:
3
Course Syllabus
Abstract
This course is an introductory course in network analysis, designed to familiarize graduate students with the general concepts and basic techniques of network analysis in sociological research, gain general knowledge of major theoretical concepts and methodological techniques used in social network analysis, and get some hands-on experience of collecting, analyzing, and mapping network data with SNA software. In addition, this course will provide ample opportunities to include network concepts in students’ master theses work.
Learning Objectives
- The goal of the course is ensure that students understand topics and principles of network analsis.
Expected Learning Outcomes
- Be able to confidently uses available data to test proposed network hypotheses.
- Be able to correctly selects appropriate model / method of network analysis for a given problem.
- Be able to develop a solid network theoretical foundation for the project at hand.
- Be able to explore the advantages and disadvantages of various network analytic tools and methods.
- Be able to integrate network information found from various sources and compensate for lack of data by adjusting models.
- Be able to master advanced research methods, including network methods, without direct supervision, and is capable of using these methods to analyze complex models.
- Have the skill to processe learned information, and integrate learned material into a cohesive research toolchest.
- Have the skills to effectively presents network research ideas to peers, instructors, and general audience.
- Have the skills to expresses network research ideas in English in written and oral communication.
- Know the advantages and disadvantages of various network analytic tools and methods.
- Know the basic principles of network analysis.
- Know the major network modeling programs.
Course Contents
- Introduction to social network analysis
- Network Analysis as a Method
- Foundational Network Measures
- Community Detection and Blockmodeling
- Social Influence Models
- Social Selection Models
- SNA software
Bibliography
Recommended Core Bibliography
- Carrington, P. J., Scott, J., & Wasserman, S. (2005). Models and Methods in Social Network Analysis. Cambridge University Press.
- Kolaczyk, E. D., & Csárdi, G. (2014). Statistical Analysis of Network Data with R. New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=783200
- Luke, D. A. (2015). A User’s Guide to Network Analysis in R. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1114415
- Nooy, W. de, Batagelj, V., & Mrvar, A. (2011). Exploratory Social Network Analysis with Pajek: Vol. Rev. and expanded 2nd ed. Cambridge University Press.
Recommended Additional Bibliography
- 9780199206650 - Newman, Mark - Networks : An Introduction - 2010 - Oxford University Press - https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=458550 - nlebk - 458550
- Kadry, S., & Al-Taie, M. Z. (2014). Social Network Analysis : An Introduction with an Extensive Implementation to a Large-scale Online Network Using Pajek. Bentham Science Publishers.
- Lazega, E., & Snijders, T. A. B. (2016). Multilevel Network Analysis for the Social Sciences : Theory, Methods and Applications. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1119294