Delivered at:: International laboratory for Applied Network Research

Course type:: Elective course

When:: 2 year, 3 module

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 develop and/or foster critical reviewing skills of published empirical research using applied statistical methods.
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.

Course Contents

The concept of randomness
Networks and Matrices
Basic models
Patterns
Statistics

Assessment Elements

Course Projects (3, varied points)
In-class labs

Interim Assessment

2021/2022 3rd module
0.6 * Course Projects (3, varied points) + 0.4 * 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, 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
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
Kolaczyk, E. D., & Csárdi, G. (2014). Statistical Analysis of Network Data with R. New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=783200
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

Master’s Programme 'Data Analytics and Social Statistics'

Contacts

Advanced Network Analysis Methods

Course Syllabus