Network Analysis

2025/2026

Учебный год

ENG

Обучение ведется на английском языке

3

Кредиты

Кто читает:

Кафедра проблем безопасности (Институт проблем безопасности)

Статус:

Курс по выбору

Когда читается:

1-й курс, 4 модуль

Преподаватели

Седова Наталья Сергеевна

Торшин Роман Викторович

Course Syllabus

Abstract

Курс знакомит студентов с активно развивающейся междисциплинарной областью исследование структурных данных и закономерностей в них. В рамках курса мы рассмотрим методы статистического и структурного анализа сетей, модели формирования и эволюции сетей и процессов, машинное обучение на графах. Особое внимание будет уделено практическому анализу и визуализации реальных сетей с использованием доступных программных средств, современных языков программирования и библиотек.

Learning Objectives

• Understand applications of SNA to organizations Understand the role of networks in various areas of work and life, and applications of SNA to various areas Understand the meaning of “social network analysis” and its applications Understand the concept of “a network”
• Understand how to interpret graphs Learn different ways to draw networks in R Understand network study designs Learn the different ways of network data collection Know the foundational network definitions”
• Learn to interpret centralities Learn how to calculate centralities in R Learn the most important measures of centrality Understand dyadic and triadic analysis, learn how to interpret the census
• Understand how to interpret social influence model parameters Learn how to build social influence models in R Understand social influence models Understand the theoretical aspects of social influence
• Know how to build ERGMs on real-life data Understand the theoretical ideas behind ERGMs Understand the importance of random graphs Understand the theoretical ideas behind network formation: social selection Understand the importance of statistical models of networks
• Know how to build blockmodels in R Know the theory behind community detection and specifically, blockmodels Understand the role of communities Understand the theoretical ideas behind community detection

Expected Learning Outcomes

• Understand applications of SNA to organizations Understand the role of networks in various areas of work and life, and applications of SNA to various areas Understand the meaning of “social network analysis” and its applications Understand the concept of “a network”
• Understand how to interpret graphs Learn different ways to draw networks in R Understand network study designs Learn the different ways of network data collection Know the foundational network definitions”
• Learn to interpret centralities Learn how to calculate centralities in R Learn the most important measures of centrality Understand dyadic and triadic analysis, learn how to interpret the census
• Understand how to interpret social influence model parameters Learn how to build social influence models in R Understand social influence models Understand the theoretical aspects of social influence
• Know how to build ERGMs on real-life data Understand the theoretical ideas behind ERGMs Understand the importance of random graphs Understand the theoretical ideas behind network formation: social selection Understand the importance of statistical models of networks
• Know how to build blockmodels in R Know the theory behind community detection and specifically, blockmodels Understand the role of communities Understand the theoretical ideas behind community detection

Course Contents

• 1. What are Networks?
• 2. Network Analysis as a Method
• 3. Foundational Network Measures
• 4. Social Influence Models
• 5. Social Selection Modeling
• 6. Community Detection Approaches

Assessment Elements

• Test
  A test after each lecture
• Project Assignment

Interim Assessment

• 2025/2026 4th module
  0.4 * Project Assignment + 0.6 * Test

Bibliography

Recommended Core Bibliography

• Applications of social network analysis. Vol.1: Individuals, , 2014
• Applications of social network analysis. Vol.2: individuals, , 2014
• Applications of social network analysis. Vol.4: Institutions, , 2014
• Carrington, P. J., Scott, J., & Wasserman, S. (2005). Models and Methods in Social Network Analysis. Cambridge: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=132264
• Fu, X., Luo, J.-D., & Boos, M. (2017). Social Network Analysis : Interdisciplinary Approaches and Case Studies. Boca Raton, FL: CRC Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1499393
• Guo, Chao, and Wolfgang Bielefeld. Social Entrepreneurship : An Evidence-Based Approach to Creating Social Value, John Wiley & Sons, Incorporated, 2014. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/hselibrary-ebooks/detail.action?docID=1637065.
• Jeremy Boissevain, & J. Clyde Mitchell. (2018). Network Analysis : Studies in Human Interaction (Vol. Reprint 2018). Berlin/Boston: De Gruyter Mouton. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1926819
• 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
• 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
• Raj P. M. K., Mohan A., Srinivasa K.G. (2018) Basics of Graph Theory. In: Practical Social Network Analysis with Python. Computer Communications and Networks. Springer, Cham. Retrieved from https://link.springer.com/chapter/10.1007%2F978-3-319-96746-2_1#citeas
• Roca Bosch, E., Julià Verdaguer, A., Villares Junyent, M., & Rosas Casals, M. (2018). Applying network analysis to assess coastal risk planning. https://doi.org/10.1016/j.ocecoaman.2018.02.001
• State of the aRt personality research: A tutorial on network analysis of personality data in R. (2015). Journal of Research in Personality, 54, 13–29. https://doi.org/10.1016/j.jrp.2014.07.003
• von, Rosing, Mark, et al. The Complete Business Process Handbook : Body of Knowledge from Process Modeling to Bpm, Elsevier Science & Technology, 2014. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/hselibrary-ebooks/detail.action?docID=1888539.
• Wasserman, S., & Faust, K. (1994). Social Network Analysis : Methods and Applications. Cambridge: Cambridge eText. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=490515
• Сетевой анализ организации : учебно-методическое пособие / составитель М. Б. Табачникова. — Воронеж : ВГУ, 2010. — 39 с. — Текст : электронный // Лань : электронно-библиотечная система. — URL: https://e.lanbook.com/book/357569 (дата обращения: 00.00.0000). — Режим доступа: для авториз. пользователей.

Recommended Additional Bibliography

• Applications of social network analysis. Vol.3: Organizations, , 2014

Authors

• SEDOVA NATALYA SERGEEVNA