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Regular version of the site

Social Network Analysis

2021/2022
Academic Year
ENG
Instruction in English
6
ECTS credits
Course type:
Elective course
When:
1 year, 3, 4 module

Instructor

Course Syllabus

Abstract

This course will introduce students to social media analysis and approaches to studying the dynamics of human behavior through the study of social media. The course begins by introducing the basic concepts and motives for modeling social phenomena as a network. Then the characteristics of social networks are considered: connectedness, resilience, centrality and density of networks. Students then move on to study the evolution of social networks, which leads to the question of analyzing the variability of collective behavior. Specific examples will be considered of how social networks can influence social change (the growth of social movements, changes in cultural norms, etc.)
Learning Objectives

Learning Objectives

  • Understanding basic concepts of social network analysis, Understanding how networks can contribute to the explanation of specific social, political, economic and cultural phenomena, Mastering basic skills of working with SNA software Gephi, Pajek, R packages, Acquaintance with biblographic network analysis software VosViewer, CitNetExplorer
Expected Learning Outcomes

Expected Learning Outcomes

  • Able to independently master new research methods, change the scientific and production profile of their activity.
  • Able to retrieve data from open statistical databases, archives, and other public sources.
  • Able to evaluate and revise learned scientific methods and methods of activity.
Course Contents

Course Contents

  • Network terminology and metrics. Describing, visualising and analysing networks. Software: Pajek, Gephi, R.
    1) Network terminology and basic concepts Definition of a network. Nodes (vertices) and links (arcs, edges). Directed and undirected relations. Representation of social relations as graphs. How to record network data: adjacency matrix, edge list, node list. 2) Network structure: centrality Egonetworks, k-neighbors. Centrality measures: in/out degree centrality; betweenness; closeness; eigenvector, Katz & PageRank. Centrality vs. centralization. Degree distribution and centralization measures. 3) Social capital What is social capital? Definitions of P.Bourdieu, J.S.Coleman, R.D.Putnam, A.Portes. Bonding and bridging; social capital formation; social capital and economic development. World Bank's programs with emphasis on social capital. Operationalization and measures of social capital. 4) Network structure: components and communities. Weak, strong, and giant components. Diffusion of information in networks. Real life examples. Network modularity. Community detection algorithms: local and global definitions, vertex dissimilarity. Clustering: hierarchical, partitional, spectral. Communities in real-life networks.
  • Network in bibliometrics: using SNA for bibliographic search and analysis. Software: VosViewer and CitNetExplorer
    5) Networks in bibliometrics. Two-mode (bimodal) networks. Affiliation matrix. Weighted networks. Bibliometric networks: co-citation, bib-coupling, co-authorship. Journal networks. Timeline in bibliometric analysis.
  • Network theory and applications. Network models. Software: Pajek, Gephi, R
    6) Network models: Random graphs (Erdos-Renuy), Small world, Preferential attachment (Power law). Network characteristics: shortest path, clusterization coefficient, degree distribution. 7) Diffusion of information in networks. Opinion formation. Social movements. Recruiting through networks. 8) Project presentations 9) Test
Assessment Elements

Assessment Elements

  • non-blocking Test
  • non-blocking Individual project (written essay) with oral presentation
  • non-blocking In-class Participation
Interim Assessment

Interim Assessment

  • Interim assessment (4 module)
    0.4 * In-class Participation + 0.35 * Individual project (written essay) with oral presentation + 0.25 * Test
Bibliography

Bibliography

Recommended Core Bibliography

  • Scott, J. (DE-588)132315661, (DE-576)299070239. (2009). Social network analysis : a handbook / John Scott. Los Angeles [u.a.]: Sage. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.307646734

Recommended Additional Bibliography

  • Kadry, S., & Al-Taie, M. Z. (2014). Social Network Analysis : An Introduction with an Extensive Implementation to a Large-scale Online Network Using Pajek. Bentham Science Publishers.