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Regular version of the site
Master 2019/2020

Social Networks

Category 'Best Course for New Knowledge and Skills'
Type: Elective course (Computational Linguistics)
Area of studies: Fundamental and Applied Linguistics
Delivered by: School of Linguistics
When: 2 year, 1, 2 module
Mode of studies: Full time
Master’s programme: Computational Linguistics
Language: English
ECTS credits: 3

Course Syllabus

Abstract

The course "Social Networks" introduces students to the new interdisciplinary field of research. Emerged in sociology, the theory of social networks in recent years, has attracted considerable interest of economists, mathematicians, physicists, experts in data analysis, computer engineers. Initially, researches focused on the study of social networks, i.e. sets of links connecting the social actors in accordance with their interaction. Nowadays, the study of actors’ relations includes economic, financial, transport, computer, language and many other networks. The course examines the methods of analyzing the structure of networks, model of their emergence and development, and the processes occurring in networks.
Learning Objectives

Learning Objectives

  • The main objective of the course «Social Networks» – to provide students with the theoretical foundations of the theory of social networks and the development of practical knowledge and skills for network science.
Expected Learning Outcomes

Expected Learning Outcomes

  • Understands the fundamental principles of social networking
  • Knows the typical applied problems considered in models of complex networks
  • Understands the capabilities and limitations of the existing network analysis methods
  • Can apply the obtained knowledge to analyze real-world networks.
Course Contents

Course Contents

  • Complex networks
    Introduction to the theory of complex systems. Basic concepts in the theory of networks. Properties and network analysis metrics. The power-law distribution. Scale-invariant network (scale-free networks). Random graphs. Pareto distribution, normalization, moments Act Tsipfa.Graf rankfrequency diameter and clustering coefficients.
  • Nodes metrics and link analysis
    Metrics and central nodes / Centrality metrics. The concepts of centrality and prestige. Model graphs. Degree centrality, closeness centrality, betweenness centrality, status / rank prestige (eigenvector centrality). Central network (sentralization). Analysis of bonds. PageRank algorithm. Stochastic matrices. Hubs and Authorities. HITS algorithm.
  • Nodes metrics and link analysis (continuation)
    Networks and semantics. Networks and syntax. Networks and morphology. Networks and phonology. Networks and applied linguistics.
  • Networks in theoretical linguistics
    Metrics and central nodes / Centrality metrics. The concepts of centrality and prestige. Model graphs. Degree centrality, closeness centrality, betweenness centrality, status / rank prestige (eigenvector centrality). Central network (sentralization). Analysis of bonds. PageRank algorithm. Stochastic matrices. Hubs and Authorities. HITS algorithm.
Assessment Elements

Assessment Elements

  • non-blocking Homework
  • non-blocking Mid Term Exam
  • non-blocking Exam
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.5 * Exam + 0.25 * Homework + 0.25 * Mid Term Exam
Bibliography

Bibliography

Recommended Core Bibliography

  • Newman, M. (2010). Networks: An Introduction. Oxford University Press, 2010

Recommended Additional Bibliography

  • Géron, A. (2017). Hands-On Machine Learning with Scikit-Learn and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems (Vol. First edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1486117