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Regular version of the site
Bachelor 2022/2023

Introduction to Network Analysis

Type: Elective course (Sociology)
Area of studies: Sociology
Delivered by: School of Sociology
When: 4 year, 3 module
Mode of studies: offline
Open to: students of one campus
Instructors: Daria Maltseva
Language: English
ECTS credits: 6
Contact hours: 30

Course Syllabus


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 sociological re-search, gain general knowledge of major theoretical concepts and methodological techniques used in social network analysis, and get some hands-on experience of collecting, analyzing, and mapping network data with SNA software. In addition, this course will provide ample opportu-nities to include network concepts in students’ master theses work.
Learning Objectives

Learning Objectives

  • The goal of the course is ensure that students understand topics and principles of network analsis.
Expected Learning Outcomes

Expected Learning Outcomes

  • Be able to confidently uses available data to test proposed network hypotheses.
  • Be able to correctly selects appropriate model / method of network analysis for a given problem.
  • Be able to develop a solid network theoretical foundation for the project at hand.
  • Be able to explore the advantages and disadvantages of various network analytic tools and methods.
  • 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 skill to processe learned information, and integrate learned material into a cohesive research toolchest.
  • Have the skills to effectively presents network research ideas to peers, instructors, and general audience.
  • Have the skills to expresses network research ideas in English in written and oral communication.
  • Know the advantages and disadvantages of various network analytic tools and methods.
  • Know the basic principles of network analysis.
  • Know the major network modeling programs.
Course Contents

Course Contents

  • Introduction
  • SNA methodology
  • SNA methodology II
  • SNA methodology III
  • SNA models I
  • SNA models II
  • Conclusion
Assessment Elements

Assessment Elements

  • non-blocking In-class Labs
    There will be a lab assignment in almost every seminar, depending on our progress. Since we will be learning R, and learning quickly, you will need to devote a substantial time to it. Seminar labs should help you with this task. At the end of the lab, you will submit your completed assignment for the day (or as much as you were able to complete) to me via LMS.
  • non-blocking Quizzes
    You cannot meaningfully participate in the seminar if you have missed my lecture and did not do any reading. Therefore, to encourage you to prepare for seminars, every seminar will have a quiz on the lecture material and all assigned readings for the week. This includes the very first seminar, which will focus on Lecture 1 material. You are allowed to miss any one quiz (skip a seminar, not prepare, etc.) – in other words, I will count the best 11 out of 12 quizzes that we will have. If you submit all twelve, I will count best nine. All quizzes will be done online and submitted to me via SurveyMonkey (links will be given in class).
  • non-blocking Final project
    The examination is carried out in writing: passing the final project through the LMS to the teacher.
  • non-blocking Homework Assignments
    In this class, homeworks are essential for learning. Simply put, you CANNOT learn statistics by simply attending the class. Homeworks will be more along the lines of the real-life problems that you will have to solve in the future, and you will have a week after the topic was introduced in class to work on these. Homework assignments are handed out in class (during seminars) and will be available electronically. I strongly recommend that you do not wait until the due date to complete those, and work on the problems a few at a time throughout the assigned period.
Interim Assessment

Interim Assessment

  • 2022/2023 3rd module
    0.2 * Homework Assignments + 0.5 * Final project + 0.1 * Quizzes + 0.2 * In-class Labs


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
  • Kolaczyk E. D., Csárdi G. Statistical analysis of network data with R. – New York : Springer, 2014. – 207 pp.
  • Luke, D. A. (2015). A User’s Guide to Network Analysis in R. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1114415
  • 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

  • Kadry, S., & Al-Taie, M. Z. (2014). Social Network Analysis : An Introduction with an Extensive Implementation to a Large-scale Online Network Using Pajek. Oak Park, IL: Bentham Science Publishers. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=694016
  • 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
  • Newman, M. (2010). Networks: An Introduction. Oxford University Press, 2010