Delivered at:: Department of Management

Course type:: Elective course

When:: 2 year, 3 module

Abstract

The network metaphor has become a prevalent part of contemporary societies. Ideas that have been developed in the social sciences over decades (e.g., on the importance of networking or the nature of social capital) are now widely accepted by the general public. However, the social network analysis (SNA) is much more than just a common metaphor. It is a powerful and well-formalized set of methods used to study complex real-world phenomena, from the spread of rumors or diseases (as seen recently with COVID-19) to the properties of global trade networks and cultural industries. SNA is an analysis-heavy subfield, meaning (1) it is challenging to come up with a general theory of social networks due to their diversity and complexity, and (2) it is difficult to grasp without hands-on experience with data. For these reasons, this course will focus on teaching you (1) how to develop researchable ideas and (2) how to test these ideas using available statistical software. The general goal of this course is thus to provide participants with essential insights into social networks and to equip them with the skills to analyze networks computationally.

Learning Objectives

After finishing the course, the students are expected to know both the popular ideas from the studies of networks and how to put them into practice. It involves getting the practical knowledge of network data structures, typical manipulations with them, calculation of node- and network- level statistics, and their accurate interpretation, embedded in a coherent research design.

Expected Learning Outcomes

To know how to pose network-related questions and develop a network-related research design. To understand possible data formats and how to theoretically organize data collection (both online and offline).
To know how to pose network-related questions and develop a network-related research design. To understand possible data formats and how to theoretically organize data collection (both online and offline). To understand basic centrality measures, how to calculate them in R, and how to relate them to a given empirical context. To know how to use network measures in regression models.
To know how to pose network-related questions and develop a network-related research design. To know how to describe networks using summary characteristics and structural properties in R. To know the algorithms for community detection (and their implementation in R) and their underlying principles. To know how to visualize network data of both small and large scales using multiple software packages (R, Gephi, Cosmograph).
To know how to pose network-related questions and develop a network-related research design. To understand possible data formats and how to theoretically organize data collection (both online and offline). To know how to visualize network data of both small and large scales using multiple software packages (R, Gephi, Cosmograph). To know the workflow for analyzing two-mode networks in R.
To know how to pose network-related questions and develop a network-related research design. To know how to visualize network data of both small and large scales using multiple software packages (R, Gephi, Cosmograph). To know the principles and approaches to blockmodeling procedures, including workflow in R.
To know how to pose network-related questions and develop a network-related research design.

Course Contents

Introduction to networks
Centrality measures and network positions
Topology and network structures
2-mode networks
Structural equivalence
Course overview and new directions

Assessment Elements

Homework
A homework answer submitted in docx or pdf format to the teacher's email address
Essay
a 3-5 page paper presenting the results of a study of network data collected independently by the student or found in existing online repositories.

Interim Assessment

2026/2027 3rd module
0.4 * Essay + 0.6 * Homework

Bibliography

Recommended Core Bibliography

Assegie, T. A., & Nair, P. S. (2019). A review on software defined network security risks and challenges. Telkomnika, 17(6), 3168–3174. https://doi.org/10.12928/TELKOMNIKA.v17i6.13119
Hidalgo, C. A., & Hausmann, R. (2013). The Atlas of Economic Complexity : Mapping Paths to Prosperity (Vol. Updated edition). Cambridge, MA: The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=686113
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

Recommended Additional Bibliography

Harvey, P., & Knox, H. (2012). The Enchantments of Infrastructure. Mobilities, 7(4), 521–536. https://doi.org/10.1080/17450101.2012.718935
Ribas, I., Lusa, A., & Corominas, A. (2019). A framework for designing a supply chain distribution network. International Journal of Production Research, 57(7), 2104–2116. https://doi.org/10.1080/00207543.2018.1530477
Ушаков, И. А. Advanced Network & Cloud Security : учебное пособие / И. А. Ушаков, А. В. Красов. — Санкт-Петербург : СПбГУТ им. М.А. Бонч-Бруевича, 2017. — 55 с. — Текст : электронный // Лань : электронно-библиотечная система. — URL: https://e.lanbook.com/book/180101 (дата обращения: 00.00.0000). — Режим доступа: для авториз. пользователей.

Authors

Kaplun Mariia Nikitichna

Master’s Programme 'Data Analytics for Business and Economics'

Contacts

Social Network Analysis

Course Syllabus