Bachelor
2022/2023
Social Network Analysis
Type:
Elective course (International Bachelor's Programme in World Politics)
Area of studies:
International Relations
Delivered by:
Faculty of World Economy and International Affairs
When:
3 year, 1, 2 module
Mode of studies:
offline
Open to:
students of one campus
Language:
English
ECTS credits:
3
Contact hours:
22
Course Syllabus
Abstract
Social network analysis is a fast-developing discipline in the field of socio-economic data analysis. It shifts the focus of scientific research from atomized individuals to their connections and brings both theoretical and methodological innovations. The rapid growth of network data - e.g. financial transactions or interactions between individuals via social media - significantly contributed to the relevance of these methods in both applied and scientific research. This course will provide students with knowledge and skills in network analysis. Selected topics include basic network analysis (centrality, positions, and clustering), the exponential random graph model for modeling network formation, and introduction to the causal analysis of network effects. By the end of the course, students will be able to apply social network analysis methods in Python to different research questions about interactions between social actors.
Learning Objectives
- The aim of this course is to demonstrate to students both theoretical rationale and important applications of network analysis methods.
Expected Learning Outcomes
- Apply basic methods and functions of Python libraries to analyze network data and complex graphs structure
- Apply the basics of social network analysis at the network level (e.g. density, clustering, degree distribution, etc.); at the node level (e.g. degree, betweenness, closeness); at the subgraph level (e.g. triads, communities)
- Collect and preprocess network data
- Design a research study on interactions between individuals and actors
Course Contents
- Week 1. Introduction to Network Science
- Week 2. Descriptive Network Analysis
- Week 3. Mathematical Models of Networks
- Week 4. Node Centrality and Ranking on Networks
- Week 5. In-class Lab
- Week 6. Network Communities
- Week 7. Network Structure and Visualization
- Week 8. Social Media and Information Flow in Networks
- Week 9. Diffusion of Innovation
- Week 10. Institutions and Aggregate Behavior in Networks
- Week 11. In-class Project Presentations
Interim Assessment
- 2022/2023 2nd module0.2 * Экзамен + 0.2 * Мини-тесты + 0.2 * Проект + 0.2 * Контрольная работа + 0.2 * Домашнее задание
Bibliography
Recommended Core Bibliography
- Easley, D., & Kleinberg, J. (2010). Networks, Crowds, and Markets : Reasoning About a Highly Connected World. New York: Cambridge eText. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=324125
- Goldenberg, D. (2021). Social Network Analysis: From Graph Theory to Applications with Python. https://doi.org/10.13140/RG.2.2.36809.77925/1
- Newman, M. E. J. (2010). Networks : An Introduction. Oxford: OUP Oxford. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=458550
Recommended Additional Bibliography
- Carrington, P. J., Scott, J., & Wasserman, S. (2005). Models and Methods in Social Network Analysis. Cambridge University Press.
- James H. Fowler, & Nicholas A. Christakis. (2009). Connected : The Surprising Power of Our Social Networks and How They Shape Our Lives: Vol. First edition. Little, Brown Spark.
- Zinoviev, D., & Tulton, A. O. (2018). Complex Network Analysis in Python : Recognize - Construct - Visualize - Analyze - Interpret. Pragmatic Bookshelf.