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Regular version of the site
Master 2021/2022

Network Analysis: Statistical Approaches

Area of studies: Applied Mathematics and Informatics
When: 1 year, 3, 4 module
Mode of studies: offline
Open to: students of one campus
Instructors: Vladimir Batagelj
Master’s programme: Applied Statistics with Network Analysis
Language: English
ECTS credits: 4
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 major theoretical concepts and methodological techniques used in creating complex network-analytic models, with hands-on experience of developing various models used to answer specific research and applied problems. In addition, this course will provide ample opportunities to include network concepts in students’ master theses work.
Learning Objectives

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.
Expected Learning Outcomes

Expected Learning Outcomes

  • Know the major network modeling programs.
  • 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.
  • Know the basic principles behind working with all types of data for building network-based models.
  • Be able to to criticize constructively and determine existing issues with applied network mdoelsin published work .
  • Have an understanding of the advantages and disadvantages of various network amodels, and demonstrate how they relate to other methods of analysis.
  • Know the basic principles of network modeling and lay the foundation for future learning in the area.
Course Contents

Course Contents

  • Models of social influence
  • Exponential Random Graph Models
  • Longitudinal models
  • Diffusion models
  • Community detection models
Assessment Elements

Assessment Elements

  • non-blocking Course Projects (3, varied points)
  • non-blocking In-class labs
Interim Assessment

Interim Assessment

  • 2021/2022 4th module
    0.4 * In-class labs + 0.6 * Course Projects (3, varied points)
Bibliography

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