Master
2024/2025
Network Science
Type:
Elective course (Data Science)
Area of studies:
Applied Mathematics and Informatics
Delivered by:
School of Data Analysis and Artificial Intelligence
Where:
Faculty of Computer Science
When:
1 year, 3, 4 module
Mode of studies:
offline
Open to:
students of one campus
Instructors:
Alexandra Kogan
Master’s programme:
Data Science
Language:
English
ECTS credits:
6
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
Interim Assessment
- 2024/2025 4th module0.33 * Домашнее задание + 0.17 * Индивидуальный проект + 0.25 * Соревнование + 0.08 * Тест + 0.17 * Экзамен