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Regular version of the site
Master 2019/2020

Advanced Network Analysis Methods

Area of studies: Applied Mathematics and Informatics
When: 1 year, 3, 4 module
Mode of studies: Full time
Instructors: Vladimir Batagelj
Master’s programme: Applied Statistics with Network Analysis
Language: English
ECTS credits: 4

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

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

Expected Learning Outcomes

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

Course Contents

  • The concept of randomness
    This session will look into sequences of random numbers, Bertrand paradox, Monte Carlo Meth-od in network analysis, variance reduction, and Monte Carlo method in statistics
  • Networks and Matrices
    The topics covered in this session will focus on semirings, matrices over semirings, networks and matrices, real matrices, and markov chains.
  • Basic models
    The session will go over Erdos-Renyi, configurational model, small worlds and scale-free net-works.
  • Patterns
    This sessions builds the understanding of subgroups, dyads, triads, indices and dissimilarities, patters, motifs, graphlets and other patterns in network.
  • Statistics
    This session covers the basics of autocorrelation, network statistics, GUG, QUAP, other issues in statistics of netoworks.
Assessment Elements

Assessment Elements

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