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KGLM 2105 - Introduction to structural analysis and social networks

Type d'enseignement : Workshop

Semester : Autumn 2017-2018

Number of hours : 12

Language of tuition : English

Pre-requisite

Students are expected to read before each session the compulsory readings regarding the topic that will be discussed in the workshop.

Course Description

The study of social networks focuses on the interdependencies between individuals, organizations and their relational context. This original perspective, that starts from the appraisal of structural properties, will be the base of our approach when analyzing regulation processes and economic activities through different case studies. This course has two objectives: (1) Introduce students to the main concepts used in social network analysis. Empirical examples will be explored to illustrate how social networks may shed light on issues such as unemployment in urban areas or the process of public regulatory policy; (2) Allow students to develop research hypotheses and analyze empirical data. R (free software) will be used for social network analysis and visualization. The course will alternate between case studies and hands-on practice.

Teachers

MONTES LIHN, Jaime (Post-doctorant)

Pedagogical format

6 classes x 2 hours

Course validation

- Active participation to class discussion (30% of final grade) - Students are requested to conduct their own research study, in groups, and to write a paper where they will analyze and discuss their results. Students will collect (through questionnaires) and analyze (with R software) social network data. This paper should include at least the following sections: Introduction, Theoretical Framework, Hypotheses, Data Analysis (of social network data), and Conclusion (70% of final grade).

Required reading

  • Session 1 : • Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge University Press. (Chapter 1 “Social Network Analysis in the Social and Behavioral Sciences” + Sections 4.1 “Why Graphs?”, 4.2 “Graphs” and 4.3 “Directed Graphs” of Chapter 4 “Graph and Matrices”)
  • Seesion2 : • An Introduction to Network Analysis with R and statnet (pages 1 to 5) https://statnet.org/trac/raw-attachment/wiki/Resources/introToSNAinR_sunbelt_2012_tutorial.pdf
  • Lazega, E. (2009), Theory of cooperation among competitors: A neo-structural approach, Sociologica http://elazega.fr/media/pdf/art/CooperationAmongCompetitorsSociologica2009.pdf
  • Seesion 3: • An Introduction to Network Analysis with R and statnet (pages 6 to 12) https://statnet.org/trac/raw-attachment/wiki/Resources/introToSNAinR_sunbelt_2012_tutorial.pdf
  • • Daraganova, G., & Pattison, P. (2013). Autologistic actor attribute model analysis of unemployment: dual importance of who you know and where you live. Exponential Random Graph Models for Social Networks: Theory, Methods and Applications, 237-247.

Additional required reading

  • Seesion 4: • Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge University Press. (Chapter 9 “Structural Equivalence”)
  • An Introduction to Network Analysis with R and statnet (pages 13 to 16) https://statnet.org/trac/raw-attachment/wiki/Resources/introToSNAinR_sunbelt_2012_tutorial.pdf
  • Penalva-Icher, E., Richard C., Cazavan-Jeny A. and Lazega, E. (2012), Banks as Masters of Debt, Cost Calculators and Risk-Sharing Mediators: A Discreet regulatory Role Observed in French Public-Private Partnerships, in Isabelle Huault and Chrystelle Richard (eds), Finance: The Discreet Regulator. How Financial Activities Shape and Transform the World, London: Palgrave-Macmillan