Social Networks

2021/2022

Учебный год

ENG

Обучение ведется на английском языке

3

Кредиты

Кто читает:

Школа лингвистики

Статус:

Курс по выбору

Когда читается:

2-й курс, 1, 2 модуль

Преподаватели

Жуков Леонид Евгеньевич

Макаров Илья Андреевич

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

• Homework
• Mid Term Exam
• Exam
• Homework
• Mid Term Exam
• Exam

Interim Assessment

• 2021/2022 2nd module
  0.5 * Exam + 0.25 * Mid Term Exam + 0.25 * Homework

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