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Course Syllabus

Master 2022/2023

Applied Network Analysis

Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Category 'Best Course for New Knowledge and Skills'
Type: Compulsory course (Applied Politics)
Area of studies: Political Science
Delivered by: School of Sociology
Where: Faculty of Social Sciences
When: 2 year, 1, 2 module
Mode of studies: offline
Open to: students of one campus
Instructors: Ekaterina Kruchinskaia
Master’s programme: Applied Politics
Language: English
ECTS credits: 7
Contact hours: 56

Course Syllabus

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

Learning Objectives

  • The purpose of this course is to provide students with an understanding of the fundamentals of network analysis and the principles of using network analysis methodology for applied research and problems.
Expected Learning Outcomes

Expected Learning Outcomes

  • Able to implicate skills in modeling.
  • Being able to access the results of modeling and interpret them sociologically.
  • Be able to explain how and why modeling is used in the support system environment.
  • Be able to correctly selects appropriate model / method of network analysis for a given problem.
  • Know the basic principles of network analysis.
  • Be able to evaluate and reprocess methods and techniques of network analysis for a given problem.
  • A student compares research design
  • A student knows the history of the discipline and subfields
  • A student knows the main approaches in the field and can use the main methods in political science
  • Students develop practical skills of network analysis in R programming language.
  • Understand basic concepts of social network analysis
  • Give examples of graph parameters and their usage in network analysis
Course Contents

Course Contents

  • Introduction
  • SNA methodology I
  • SNA methodology II
  • SNA methodology III
  • SNA models I
  • SNA models II
  • SNA Projects and Real Cases
  • Conclusion
Assessment Elements

Assessment Elements

  • non-blocking Final Project
    The examination for this course is a presentation of a final project. Detailed information on topic presentations will be provided in class.
  • non-blocking Homework
  • non-blocking Test
  • non-blocking In-Class Activity
Interim Assessment

Interim Assessment

  • 2022/2023 2nd module
    0.4 * Final Project + 0.1 * Homework + 0.1 * Test + 0.1 * In-Class Activity
Bibliography

Bibliography

Recommended Core Bibliography

  • Advances in network clustering and blockmodeling, , 2020
  • Aleskerov, F., Meshcheryakova, N., & Shvydun, S. (2016). Centrality measures in networks based on nodes attributes, long-range interactions and group influence.
  • Analyzing social networks, Borgatti, S. P., 2018
  • Applications of social network analysis. Vol.1: Individuals, , 2014
  • Applications of social network analysis. Vol.2: individuals, , 2014
  • Applications of social network analysis. Vol.3: Organizations, , 2014
  • Applications of social network analysis. Vol.4: Institutions, , 2014
  • 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
  • Complex social networks, Vega-Redondo, F., 2007
  • Constrained principal component analysis and related techniques, Takane, Y., 2014
  • Granovetter, M. (1983). The Strength of Weak Ties: a Network Theory Revisited. Sociological Theory, 201. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=sih&AN=10313288
  • 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
  • Models and methods in social network analysis, , 2006
  • Political networks : the structural perspective, Knoke, D., 2003
  • Principal component neural networks : theory and applications, Diamantaras, K. I., 1996
  • Social network analysis : methods and applications, Wasserman, S., 2007

Recommended Additional Bibliography

  • Cluster analysis, Everitt, B. S., 2011
  • Generalized blockmodeling, Doreian, P., 2005
  • J. A. Bondy, & U. S. R. Murty. (1976). Graph theory with applications. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.CB6871BA
  • Newman, M. (2010). Networks: An Introduction. Oxford University Press, 2010

Authors

  • KRUCHINSKAYA EKATERINA VLADISLAVOVNA
  • ZAYTSEV DMITRIY GENNADEVICH