Магистратура
2024/2025
Статистические методы сетевого анализа
Лучший по критерию «Новизна полученных знаний»
Статус:
Курс по выбору (Аналитика данных и прикладная статистика / Data Analytics and Social Statistics)
Направление:
01.04.02. Прикладная математика и информатика
Где читается:
Факультет социальных наук
Когда читается:
2-й курс, 1 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Прогр. обучения:
Прикладная статистика с методами сетевого анализа
Язык:
английский
Кредиты:
3
Course Syllabus
Abstract
The cores explores probabilistic and mathematical foundations for modeling and analyzing complex networks. The course covers key topics such as randomness (Monte Carlo methods, paradoxes), matrix representations of networks (semirings, Markov chains), and foundational models (Erdős-Rényi, scale-free networks). It also delves into statistical tools for pattern detection (motifs, graphlets), hypothesis testing (GUG, QUAP), and advanced techniques like ERGMs and SAOMs for dynamic and social network analysis. 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.
- 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.
Interim Assessment
- 2024/2025 1st module0.33 * Course project 1 + 0.33 * Course project 2 + 0.34 * Course project 3
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.