Анализ социальных сетей

Магистратура 2019/2020

Статус: Курс по выбору (Сравнительные социальные исследования)

Направление: 39.04.01. Социология

Кто читает: Департамент социологии

Где читается: Факультет социальных наук

Когда читается: 1-й курс, 3 модуль

Формат изучения: без онлайн-курса

Преподаватели: Зайцев Дмитрий Геннадьевич

Прогр. обучения: Сравнительные социальные исследования

Язык: английский

Кредиты: 5

Контактные часы: 40

Full Syllabus

Abstract

This course is an introductory course in network analysis, designed to familiarize graduate students with the general concepts and basic techniques of network analysis in political research, gain general knowledge of major theoretical concepts and methodological techniques used in social network analysis (SNA), and get some hands-on experience of collecting, analyzing, and mapping network data with SNA software. In addition, this course will provide ample opportunities to include network concepts in students’ master theses work.

Learning Objectives

The goal of the course is to ensure that students understand topics and principles of network analysis

Expected Learning Outcomes

Know the basic principles of network analysis
Know the advantages and disadvantages of various network analytic tools and methods
Know the major network modeling programs
Be able to explore the advantages and disadvantages of various network analytic tools and methods
Be able to correctly selects appropriate model / method of network analysis for a given problem
Be able to confidently use available data to test proposed network hypotheses
Be able to develop a solid network theoretical foundation for the project at hand
Be able to integrate network information found from various sources and compensate for lack of data by adjusting models
Be able to master advanced research methods, including network methods, without direct supervision, and is capable of using these methods to analyze complex models
Have the skills to expresses network research ideas in English in written and oral communication
Have the skills to effectively presents network research ideas to peers, instructors, and general audience
Have the skill to process learned information, and integrate learned material into a cohesive research toolset

Course Contents

Introduction
Social network analysis: Methods or theory? Structural approach. Interdisciplinary interest in network analysis. Network theories most popular in social sciences. Key network concepts: network, structure, nodes, ties, sociogram, structural and compositional variables, etc. Types of network data. Sampling and data collection in network analysis.
SNA methodology I
Survey instruments for collecting network data. Network data collection and ethical issues. Basic measures of network characteristics. Graphic representation of network relations.
SNA methodology II
Network measures for dyads and triads. Identifying tightly connected groups and subgroups in social networks. Small-world phenomenon. Homophily principal in personal relationships. Cultural and historical differences in network connectivity. Personal ties and social support.
SNA methodology III
Centrality and Influence. Measures of Centrality. Two-mode networks: transformation, graphical representation, and analysis. Centrality and twomode networks in the studies of power and influence.
SNA models I
The strength of weak ties. Social capital. Structural holes. Examples of social network analysis in political science. Political networks. Policy networks. Networks in International Relations.
SNA models II
Blockmodeling and clustering. Cluster analysis on networks. Principal component analysis
SNA models III
Social influence model. Social selection model. Exponential random graph models (ERGM).
Conclusion
This session will bring all approaches to social network analysis together and apply it to the students’ current projects.

Assessment Elements

Course Project
Homework Assignments (5 x Varied points)
In-Class Labs (9-10 x Varied points)
Quizzes (Best 9 of 10, Varied points)

Interim Assessment

Interim assessment (3 module)
0.5 * Course Project + 0.2 * Homework Assignments (5 x Varied points) + 0.2 * In-Class Labs (9-10 x Varied points) + 0.1 * Quizzes (Best 9 of 10, Varied points)

Bibliography

Recommended Core Bibliography

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
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
Nooy, W. de, Mrvar, A., & Batagelj, V. (2005). Exploratory Social Network Analysis with Pajek. New York: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=138973

Recommended Additional Bibliography

Newman, M. (2010). Networks: An Introduction. Oxford University Press, 2010

Course Syllabus