Delivered at:: International Laboratory for Applied Network Research

Course type:: Elective course

When:: 2 year, 1 module

Instructors

Maltseva, Daria

Semenova, Anna

Full Syllabus Ask Question

Abstract

Mathematical models based on probability theory prove to be extremely useful in describing and analyzing complex systems that exhibit random components. The goal of this course is to introduce several classes of stochastic processes, analyze their behavior over a finite or infinite time horizon, and help students enhance their problem solving skills. The course combines classic topics such as martingales, Markov chains, renewal processes, and queuing systems with approaches based on Stein’s method and on concentration inequalities. The course focuses mostly on discrete-time models and explores a number of applications in operations research, finance, and engineering. This is an elective course, offered to MASNA and DASS students, and examples used in class may differ depending on students’ interests.

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 major network modeling programs.
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.
Know the basic principles behind working with all types of data for building network-based models.
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.
Know the basic principles of network modeling and lay the foundation for future learning in the area.

Course Contents

The concept of randomness
Networks and Matrices
Basic models
Patterns
Statistics

Assessment Elements

Course project 1
Course project 2
Course project 3

Interim Assessment

2024/2025 1st module
0.33 * Course project 1 + 0.33 * Course project 2 + 0.34 * Course project 3

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.

Recommended Additional Bibliography

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. Statistical analysis of network data with R. – New York : Springer, 2014. – 207 pp.

Authors

Klimov Ivan Aleksandrovich
MALTSEVA Daria VASILEVNA
Pavlova Irina Anatolevna
SEMENOVA ANNA MIKHAILOVNA

Master’s Programme 'Data Analytics and Social Statistics'

Contacts

Network Analysis: Statistical Approaches