Бакалавриат
2021/2022
Анализ социальных сетей
Статус:
Курс по выбору
Направление:
41.03.06. Публичная политика и социальные науки
Кто читает:
Факультет мировой экономики и мировой политики
Где читается:
Факультет мировой экономики и мировой политики
Когда читается:
3-й курс, 1, 2 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Преподаватели:
Чмель Кирилл Шамилевич
Язык:
английский
Кредиты:
3
Контактные часы:
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
Assessment Elements
- Take-home Assignment 1100 points each; week 3, 7.
- In-class Lab100 points; week 5.
- In-class Quizzes10 points each, 10 quizzes.
- FInal Project100 points
- In-class Project Presentation100 points
- Take-home Assignment 2100 points each; week 3, 7
Interim Assessment
- 2021/2022 2nd moduleThe final grade will be computed as Final Grade = 0.2*Take-home Assignment 1 + 0.2*Take-home Assignment 2 + 0.15*In-class Lab + 0.15*In-class Quizzes + 0.25*FInal Project + 0.05*In-class Project Presentation. The final score will be transformed to the HSE regular scale according to the following rule: 0-19 - 1 20-29 - 2 30-39 - 3 40-49 - 4 50-59 - 5 60-69 - 6 70-79 - 7 80-89 - 8 90-95 - 9 96-100 - 10
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.