2025/2026
Статистические методы сетевого анализа
Лучший по критерию «Полезность курса для Вашей будущей карьеры»
Лучший по критерию «Полезность курса для расширения кругозора и разностороннего развития»
Лучший по критерию «Новизна полученных знаний»
Статус:
Маго-лего
Когда читается:
1 модуль
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
3
Контактные часы:
40
Course Syllabus
Abstract
The course explores probabilistic and statistical foundations for modeling and analyzing complex networks. It covers key topics such as network representation and descriptive statistics, hypothesis testing for network metrics, and null models based on random graphs. The course also delves into community detection algorithms and their evaluation, exponential random graph models (ERGM) – from specification to estimation and interpretation – and dynamic network approaches including TERGM and SAOM for longitudinal and actor-oriented modeling. The course bridges theory and applications in social, biological, and technological networks.
Learning Objectives
- The main goal of the class is to help students, who are already familiar with network theory and methods, to use the integrated systems thinking approach to create theoretically driven, methodologically sound research projects.
Expected Learning Outcomes
- Know the major network modeling programs.
- Be able to develop and code the appropriate model to answer the stated research question.
- Be able to identify a model that is appropriate for a research problem.
- Be able to work with major network modeling programs, especially R, so that they can use them and interpret their output.
- Have a working knowledge of the different ways to analyze the network data.
- Have an understanding of the advantages and disadvantages of various network models, and demonstrate how they relate to other methods of analysis.
- Know the basic principles behind working with all types of data for building network-based models.
- Be able to develop and/or foster critical reviewing skills of published empirical research using applied statistical methods.
- Be able to to criticize constructively and determine existing issues with applied network mdoelsin published work .
- Have an understanding of the advantages and disadvantages of various network amodels, and demonstrate how they relate to other methods of analysis.
- Know the basic principles of network modeling and lay the foundation for future learning in the area.
- Understanding how the networks form (network models)
Course Contents
- Introduction to Statistical Thinking in Networks
- Network Representation and Graph Descriptive Statistics
- Statistical tests for network metrics
- Null models: Random graphs
- Community detection: Algorithms and their evaluation
- Introduction to ERGM
- Dynamic networks and actor-oriented modeling: TERGM and SAOM
Interim Assessment
- 2025/2026 1st module0.33 * Course project 2 + 0.34 * Course project 3 + 0.33 * Course project 1
Bibliography
Recommended Core Bibliography
- Dehmer, M., & Basak, S. C. (2012). Statistical and Machine Learning Approaches for Network Analysis. Hoboken, N.J.: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=465414
- Mesbahi, M., & Egerstedt, M. (2010). Graph Theoretic Methods in Multiagent Networks. Princeton: Princeton University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=816475
- 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
- Kadry, S., & Al-Taie, M. Z. (2014). Social Network Analysis : An Introduction with an Extensive Implementation to a Large-scale Online Network Using Pajek. Oak Park, IL: Bentham Science Publishers. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=694016
- Kolaczyk E. D., Csárdi G. Statistical analysis of network data with R. – New York : Springer, 2014. – 207 pp.