Network Science

2025/2026

Учебный год

ENG

Обучение ведется на английском языке

6

Кредиты

Кто читает:

Департамент анализа данных и искусственного интеллекта

Статус:

Маго-лего

Когда читается:

3, 4 модуль

Преподаватели

Коган Александра Сергеевна

Поздняков Виталий Витальевич

Course Syllabus

Abstract

The course “Network Science” introduces students to new and actively evolving interdisciplinary field of network science. Started as a study of social networks by sociologists, it attracted attention of physicists, computer scientists, economists, computational biologists, linguists and others and become a truly interdisciplinary field of study. In spite of the variety of processes that form networks, and objects and relationships that serves as nodes and edges in these networks, all networks poses common statistical and structural properties. The interplay between order and disorder creates complex network structures that are the focus of the study. In the course we will consider methods of statistical and structural analysis of the networks, models of network formation and evolution and processes developing on network. Special attention will be given to the hands-on practical analysis and visualization of the real world networks using available software tools and modern programming languages and libraries.

Learning Objectives

• To familiarize students with a new rapidly evolving filed of network science, and provide practical knowledge experience in analysis of real world network data.

Expected Learning Outcomes

• Know basic notions and terminology used in network science
• Understand fundamental principles of network structure and evolution.
• Can develop mathematical models of network processes.
• Can analyze real world network data.

Course Contents

• Introdiction to network science
• Scale-free networks
• Random networks
• Network models
• Node centrality and ranking on networks
• Structural properties of networks
• Community detection in networks
• Epidemics on networks
• Cascades and influence maximization
• Node classification
• Link prediction
• Graph embedding
• Graph neural networks
• GNNs in practice
• Theoretical foundations of GNNs
• Knowledge graphs

Assessment Elements

• Домашнее задание
• Тест
• Индивидуальный проект
• Соревнование
• Экзамен

Interim Assessment

• 2025/2026 4th module
  0.25 * Экзамен + 0.17 * Домашнее задание + 0.08 * Индивидуальный проект + 0.33 * Тест + 0.17 * Соревнование

Bibliography

Recommended Core Bibliography

• A first course in machine learning, Rogers, S., 2012

Recommended Additional Bibliography

• Network science, Barabasi, A.-L., 2019

Authors

• Антропова Лариса Ивановна