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Regular version of the site
Bachelor 2020/2021

Social Network Analysis

Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Category 'Best Course for New Knowledge and Skills'
Type: Elective course (Sociology and Social Informatics)
Area of studies: Sociology
When: 2 year, 4 module
Mode of studies: offline
Instructors: Maria Safonova
Language: English
ECTS credits: 5
Contact hours: 40

Course Syllabus

Abstract

The course is basic course in social network analysis with special attention to exemplary empirical studies, including intra-organizational networks, creative networks, criminal networks, networks of political mobilizations etc.
Learning Objectives

Learning Objectives

  • receive an overview of exemplary empirical studies designed with SNA methodology
Expected Learning Outcomes

Expected Learning Outcomes

  • abilities (a) to apply methods of social network analysis to sociological data and (b) develop research design within social network analysis framework
Course Contents

Course Contents

  • Basic concepts of social network analysis
    Nodes. Edges. Matrix. Graph. Observable and perceptive networks. Macro, meso- and micro-levels of analysis. Types of network data. Full network data & ego-network data. Network boundary: realist and nominalist approaches. Sampling in network analysis. Attributes. Measuring links. Sources of network data.
  • Centrality & centralization
    Centrality & centralization. In-degree & out-degree. Farness and closeness. Node betweenness & edge betweenness. Eigenvector centrality. Bonacich power.
  • Cohesion
    Cohesion in networks. Density. Geodesic distance. Diameter. Reciprocity and transitivity. Node’s attributes, groups & homophily. Group’s external and internal ties. Assortativity.
  • Ego-networks & structural holes
    Defining ego-network. Ego-networks size and density. Redundancy, effective size and efficiency. Inefficiency as a function of size, density, hierarchy & structural equivalency. Structural holes and constraint.
  • Community structure
    Top-down and bottom-up approaches to community structure. Cliques, n-cliques, clique percolation ect. Componets. Fractions. Modularity maximization. Goodness of fit measures.
  • Equivalent positions & block-modelling
    Positions and roles in network analysis. Equivalent positions. Structural and regular equivalency. Blocks. Block-matrix and image-matrix. Goodness of fit. Coalition, hierarchy, community structure & isolation.
  • Multiplex & two-mode networks
    Matrix operations. Two-mode to one-mode. Projection. Two-mode centralities, density, community structure.
  • Statistics & modelling in SNA
    Monadic, dyadic and mixed hypothesis.
Assessment Elements

Assessment Elements

  • non-blocking Seminar1 grade
    home reading presentations during classes & class discussions
  • non-blocking Seminar2 grade
    seminar project presentations
  • non-blocking Discussion
  • non-blocking Final exam
    form of a written research report discussing network measures & their interpretations
Interim Assessment

Interim Assessment

  • Interim assessment (4 module)
    0.22 * Discussion + 0.2 * Final exam + 0.29 * Seminar1 grade + 0.29 * Seminar2 grade
Bibliography

Bibliography

Recommended Core Bibliography

  • Luke, D. A. (2015). A User’s Guide to Network Analysis in R. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1114415

Recommended Additional Bibliography

  • Etaner-Uyar, A. S., & Gündüz-Ögüdücü, S. (2014). Social Networks: Analysis and Case Studies. Wien: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=812923