Магистратура
2024/2025
Продвинутые методы сетевого анализа в Pajek
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:
1-й курс, 4 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Преподаватели:
Мальцева Дарья Васильевна
Прогр. обучения:
Аналитика данных и прикладная статистика
Язык:
английский
Кредиты:
3
Course Syllabus
Abstract
This course is an advanced network analysis course, designed for master's programme students who are familiar with concepts and basic techniques of network analysis in applied context with Pajek software aimed for analysis and visualization of the large network data. The course provides an advanced view of social network analysis covering all basic sections of SNA methodology. In addition, this course will provide ample opportunities to include network concepts in students’ master theses work.
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
- The course gives students an important foundation to develop and conduct their own research as well as to evaluate research of others.
Expected Learning Outcomes
- Know the major network modeling programs.
- Be able to criticize constructively and determine existing issues with applied network mdoelsin published work
- 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.
- Know the basic principles of network statistical analysis.
- Be able to develop and/or foster critical reviewing skills of published empirical research using applied statistical methods.
Course Contents
- Description of networks
- Sources of networks
- Subnetworks
- Connectivity
- Cohesion
- Acyclic networks and patterns search
- Two-mode networks and multiplication
- Clustering and blockmodeling
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.
- Robins, G., Koskinen, J., & Lusher, D. (2012). Exponential Random Graph Models for Social Networks : Theory, Methods, and Applications. Cambridge: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=498293
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
- 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
- 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
- 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