Бакалавриат
2025/2026
Вычислительные социальные сети
Статус:
Курс обязательный (Вычислительные социальные науки)
Кто читает:
Департамент политики и управления
Где читается:
Факультет социальных наук
Когда читается:
4-й курс, 3 модуль
Охват аудитории:
для своего кампуса
Преподаватели:
Седашов Евгений Александрович
Язык:
английский
Кредиты:
3
Контактные часы:
40
Course Syllabus
Abstract
This class serves as the introduction to the computational methods used in the social networks research. Class will cover basic algorithms such as Louvain algorithm and various approaches to centrality measures. Theoretical discussion of computational approaches will be accompanied by practical illustrations based on published research from top journals. Some of the topics from inferential network analysts will also be covered.
Learning Objectives
- The main goal of this class is for students to learn the most important computational methods of social network analysis. The focus will be on solving actual analytical problems with tools availabe in modern software.
Expected Learning Outcomes
- Knows key concepts of social network analysis (nodes, edges, graphs)
- Knows graph theory fundamentals: types of graphs and basic metrics of degree centrality
- Knows basic network models: random graphs, small-world networks, scale-free networks
- Knows methods for collecting social network data (APIs, web-scraping), data cleaning, and preprocessing.
- Knows techniques for visualizing social networks and corresponding tools (Gephi, NetworkX).
- Knows algorithms for community detection (modularity, spectral clustering).
- Knows models of information diffusion and influence maximization in networks
- Knows the main SNA metrics and their applications (betweenness, closeness, eigenvector centrality).
- Knows main applications of machine learning algorithms in social networks analysis.
Course Contents
- Introduction to Social Networks
- Graph Theory Basics
- Networks Models
- Data Collection and Preprocessing
- Network Visualization
- Community Detection
- Influence and Diffusion in Networks
- Social Network Analysis (SNA) Techniques
- Machine Learning in Social Networks
- Case Studies in Computational Social Networks
