• A
  • A
  • A
  • АБB
  • АБB
  • АБB
  • А
  • А
  • А
  • А
  • А
Обычная версия сайта
Бакалавриат 2021/2022

Анализ социальных сетей

Статус: Курс по выбору
Направление: 41.03.06. Публичная политика и социальные науки
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

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

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

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

Assessment Elements

  • non-blocking Take-home Assignment 1
    100 points each; week 3, 7.
  • non-blocking In-class Lab
    100 points; week 5.
  • non-blocking In-class Quizzes
    10 points each, 10 quizzes.
  • non-blocking FInal Project
    100 points
  • non-blocking In-class Project Presentation
    100 points
  • non-blocking Take-home Assignment 2
    100 points each; week 3, 7
Interim Assessment

Interim Assessment

  • 2021/2022 2nd module
    The 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

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.