Master 2022/2023

Social Networks

Type: Elective course (Digital Humanities)

Area of studies: Fundamental and Applied Linguistics

Delivered by: School of Linguistics

Where: Faculty of Humanities

When: 2 year, 1, 2 module

Mode of studies: offline

Open to: students of all HSE University campuses

Instructors: Vitaliy Pozdnyakov, Leonid E Zhukov

Master’s programme: Цифровые методы в гуманитарных науках

Language: English

ECTS credits: 3

Contact hours: 32

Course Syllabus

Abstract

The course "Social Networks" introduces students to the new interdisciplinary field of research. Emerged in sociology, the theory of social networks in recent years, has attracted considerable interest of economists, mathematicians, physicists, experts in data analysis, computer engineers. Initially, researches focused on the study of social networks, i.e. sets of links connecting the social actors in accordance with their interaction. Nowadays, the study of actors’ relations includes economic, financial, transport, computer, language and many other networks. The course examines the methods of analyzing the structure of networks, model of their emergence and development, and the processes occurring in networks.

Learning Objectives

• The main objective of the course «Social Networks» – to provide students with the theoretical foundations of the theory of social networks and the development of practical knowledge and skills for network science.

Expected Learning Outcomes

• Can apply the obtained knowledge to analyze real-world networks.
• Knows the typical applied problems considered in models of complex networks
• Understands the capabilities and limitations of the existing network analysis methods
• Understands the fundamental principles of social networking

Course Contents

• Complex networks
• Nodes metrics and link analysis
• Nodes metrics and link analysis (continuation)
• Networks in theoretical linguistics

Assessment Elements

• Домашнее задание
• Письменная работа
• Практическая работа
  Презентация собственного анализа

Interim Assessment

• 2022/2023 2nd module
  Домашнее задание 0,5 Письменная рабоота 0,25 Практическая работа 0,25

Bibliography

Recommended Core Bibliography

• Newman, M. (2010). Networks: An Introduction. Oxford University Press, 2010

Recommended Additional Bibliography

• Géron, A. (2017). Hands-On Machine Learning with Scikit-Learn and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems (Vol. First edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1486117