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

Delivered by: Department of Sociology

Where: Saint-Petersburg School of Social Sciences

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

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

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

• 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

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

Interim Assessment

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

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