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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

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

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

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

Assessment Elements

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

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
Bibliography

Bibliography

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.

Recommended Additional Bibliography

  • Barabási A. L., Frangos J. Linked: the new science of networks science of networks. – Basic Books, 2002. – 211 pp.
  • Zuur, A., Ieno, E. N., Meesters E. A Beginner's Guide to R. – Springer Science & Business Media, 2009. – 240 pp.