Магистратура
2024/2025
Статистические методы сетевого анализа
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
Контактные часы:
40
Course Syllabus
Abstract
Mathematical models based on probability theory prove to be extremely useful in describing and analyzing complex systems that exhibit random components. The goal of this course is to introduce several classes of stochastic processes, analyze their behavior over a finite or infinite time horizon, and help students enhance their problem solving skills. The course combines classic topics such as martingales, Markov chains, renewal processes, and queuing systems with approaches based on Stein’s method and on concentration inequalities. The course focuses mostly on discrete-time models and explores a number of applications in operations research, finance, and engineering. This is an elective course, offered to MASNA and DASS students, and examples used in class may differ depending on students’ interests.
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.