Master 2023/2024

Advanced Network Analysis Methods

Type: Elective course

Area of studies: Applied Mathematics and Informatics

Delivered by: International laboratory for Applied Network Research

Where: Faculty of Social Sciences

When: 1 year, 4 module

Mode of studies: offline

Open to: students of one campus

Master’s programme: Applied Statistics with Network Analysis

Language: English

ECTS credits: 3

Contact hours: 40

Course Syllabus

Abstract

This course is an advanced network analysis course, designed for MASNA students who are familiar with concepts and basic techniques of network analysis in applied context. The course provides an advanced view of statistical approaches to network analysis. In addition, this course will provide ample opportunities to include network concepts in students’ master theses work. The ultimate outcome of the class is the completed project proposal for a study, which can later be completed as a full-scale research project.

Learning Objectives

• The main goal of the class is to help students, who are already familiar with network theory and methods, to use the integrated systems thinking approach to create theoretically driven, methodologically sound research projects
• The course gives students an important foundation to develop and conduct their own research as well as to evaluate research of others.

Expected Learning Outcomes

• Know the major network modeling programs.
• Be able to criticize constructively and determine existing issues with applied network mdoelsin published work
• Be able to develop and code the appropriate model to answer the stated research question.
• Be able to identify a model that is appropriate for a research problem.
• Be able to work with major network modeling programs, especially R, so that they can use them and interpret their output.
• Have a working knowledge of the different ways to analyze the network data.
• Have an understanding of the advantages and disadvantages of various network models, and demonstrate how they relate to other methods of analysis.
• Know the basic principles behind working with all types of data for building network-based models.
• Know the basic principles of network statistical analysis.
• Be able to develop and/or foster critical reviewing skills of published empirical research using applied statistical methods.

Course Contents

• The concept of randomness
• Networks and Matrices
• Basic models
• Patterns
• Statistics

Assessment Elements

• Course Projects
• In-class labs

Interim Assessment

• 2023/2024 4th module
  0.5 * Course Projects + 0.5 * In-class labs

Bibliography

Recommended Core Bibliography

• Dehmer, M., & Basak, S. C. (2012). Statistical and Machine Learning Approaches for Network Analysis. Hoboken, N.J.: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=465414
• Mesbahi, M., & Egerstedt, M. (2010). Graph Theoretic Methods in Multiagent Networks. Princeton: Princeton University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=816475
• Nooy, W. de, Batagelj, V., & Mrvar, A. (2011). Exploratory Social Network Analysis with Pajek: Vol. Rev. and expanded 2nd ed. Cambridge University Press.
• Robins, G., Koskinen, J., & Lusher, D. (2012). Exponential Random Graph Models for Social Networks : Theory, Methods, and Applications. Cambridge: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=498293

Recommended Additional 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
• 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
• 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
• 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