Introduction to Network Analysis

Bachelor 2022/2023

Type: Elective course (Sociology)

Area of studies: Sociology

Delivered by: School of Sociology

Where: Faculty of Social Sciences

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

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 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

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

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

Introduction
SNA methodology
SNA methodology II
SNA methodology III
SNA models I
SNA models II
Conclusion

Assessment Elements

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.
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).
Final project
The examination is carried out in writing: passing the final project through the LMS to the teacher.
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

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

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
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

Course Syllabus