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Regular version of the site
Master 2020/2021

## Network Analysis: Statistical Approaches

Area of studies: Applied Mathematics and Informatics
When: 2 year, 3 module
Mode of studies: offline
Instructors: Valentina Kuskova
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 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

• 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

• Know the basic principles of network modeling and lay the foundation for future learning in the area.
• 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.
• 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.
• Have a working knowledge of the different ways to analyze the network data.

#### Course Contents

• Models of social influence
This session will focus on how to use network variables as inputs (predictors), explaining variance in some non-network variable(s). The models covered will include basic linear modeling and factor analysis, and will focus on the empirical meaning of network characteristics such as centrality.
• Exponential Random Graph Models
The topics covered in this session will focus on exponential random graph models, with extentions to temporal (TERMG) and separable TERMG (STERGM). Another topic in this session is on ERGM for ego networks.
• Longitudinal models
The session will go over the theoretical and analytic specifications of longitudinal modeling, SIENA.
• Diffusion models
This sessions builds the understanding of diffusion through network, including modeling the spread of a single product (threshold models, influence maximization problem) and diffusion models in the presence of competition (algorithmic and game-theoretical aspects, extensions of the basic threshold and cascade models, study of equilibrium behavior).
• Community detection models
This session covers the basics of local analysis (dyads, triads), extending it to blockmodels, clus-tering, spinglass algorithm, exploratory graph analysis, and social community detection.

#### Assessment Elements

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

#### Interim Assessment

• Interim assessment (3 module)
0.6 * Course Projects (3, varied points) + 0.4 * In-class labs

#### 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