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Regular version of the site
2021/2022

## Social Network Analysis

Type: Mago-Lego
When: 4 module
Open to: students of all HSE University campuses
Instructors: Ilia Karpov
Language: English
ECTS credits: 3

### Course Syllabus

#### Abstract

Курс знакомит студентов с активно развивающейся междисциплинарной областью исследования структурных данных и закономерностей в них. В рамках курса мы рассмотрим методы статистического и структурного анализа сетей, модели формирования и эволюции сетей и процессов, машинное обучение на графах. Особое внимание будет уделено практическому анализу и визуализации реальных сетей с использованием доступных программных средств, современных языков программирования и библиотек.

#### Learning Objectives

• Providing students with essential knowledge of network analysis applicable to real world data, with examples from today’s most popular social networks.

#### Expected Learning Outcomes

• Students know basic notation and terminology used in network science.
• Students understand basic principles behind network analysis algorithms.
• Students develop practical skills of network analysis in R programming language.
• Students visualize, summarize and compare networks.
• Students analyze real work networks.

#### Course Contents

• Introduction to network science
Introduction to network science. Examples.
• Descriptive network analysis
Basic graph theory notations. Node degree. Node degree distribution. Power laws. Scale free networks. Connected components. Graph diameter. Average path length. Local and global clustering coefficients. Transitivity.
• Mathematical models of networks
Erdos-Reni random graph model. Bernoulli distribution. Phase transition, gigantic connected component. Diameter and cluster coefficient. Barabasi-Albert model. Preferential attachement. Small world model. Watts-Strogats model. Transition from regular to random.
• Node centralitiy and ranking on network
Node centrality metrics, degree centrality, closeness centrality, betweenness centrality, eigenvector centrality. Katz status index and Bonacich centrality, alpha centrality PageRank,Hubs and Authorites.
• Network communities
Cohesive subgroups. Graph cliques. Network communities. Graph partitioning. Modularity. Edge Betweenness. Spectral partitioning. Modularity maximization. Heuristic methods. Label propagation. Fast community unfolding. Walktrap.
• Epidemics and information spreading in networks
Epidemic models on networks. SI, SIS, SIR models. Rumor spreading. Propagation trees.
• Diffusion of innovation
Diffusion of innovation. Linear threshold model. Influence maximization.
• Spatial models of segregation
Schelling's segregation model. Spatial segregation. Agent based modelling. Segregation in networks.

#### Assessment Elements

• Homework 1
• Homework 2
• Homework 3
• Homework 4
• Exam
Экзамен проводится письменно путем отправки тестов на электронную почту студентов за 15 минут до начала экзамена.

#### Interim Assessment

• Interim assessment (4 module)
0.4 * Exam + 0.15 * Homework 1 + 0.15 * Homework 2 + 0.15 * Homework 3 + 0.15 * Homework 4

#### Recommended Core Bibliography

• Easley, D. et al. Networks, crowds, and markets. – Cambridge : Cambridge university press, 2010. – 744 pp.
• Kolaczyk E. D., Csárdi G. Statistical analysis of network data with R. – New York : Springer, 2014. – 207 pp.