Social Network Analysis: theories, methods and practices 3500-FAKANG-SNA-SCC
From friendships, rivalries, romantic relationships and Florentine families, through informal organizational hierarchies, collegiality in law-firms and membership in large-scale groups, to collections of archival texts, talk among weakly connected acquaintances and cultural processes – we all live in an interconnected world. It is necessary to be able to critically assess which of those relations are of interest to us and can be sensibly researched.
This course serves as a comprehensive introduction to the best practices of Social Network Analysis. We’ll cover key theories and methods, but always with an eye on empirical application and specific research interests of the students. The main body of the course consists of three parts.
First part gives on overview of most important and useful research concepts and tools (i.e. ego-centered measures, positions, roles, local configurations, structural mechanisms, actor attributes, processes on networks, network stability and change). Theories and concepts will be introduced together with methods and empirical applications. This way students will not only get to know the basic tools, but also how to use them, and how to do it well.
Second part is devoted to critical assessment of data, its sources, curation, manipulations and best analytical practices. Here we will get to know how to store and access relational data, manipulate and extract meaning from matrices and use R and/or Python packages devoted to network analysis.
Third part consists of four meetings devoted to specific research applications, as well as one meeting focused on presentation of network data. Each part will start with classical problems of SNA, but focus mostly on newer developments and cutting-edge research perspectives. Students will be asked to prepare short presentations of topics related to their own interests and discuss how they can be used in network research.
After completion, students will be able to assess different types of relations and relational problems, discriminate among data sources, gather relevant literature and frame specific questions. This course will provide them with enough network literacy to detect poorly framed research questions, anomalies in the data, untrue interpretations and misleading network visualizations. Course materials provided by the teacher (including reusable code, solutions to harder exercises and datasets) will cover all its parts and allow for participation.
Rodzaj przedmiotu
Tryb prowadzenia
Założenia (opisowo)
Koordynatorzy przedmiotu
Efekty kształcenia
K_W03 Has in-depth knowledge about social structures and selected social institutions as well as their interrelations
K_U02 Can critically select information and materials for academic work, using various sources in Polish and a foreign language as well as modern technologies
K_U03 Can independently form and verify judgments about the causes of selected social phenomena
K_U06 Can use a selected computer program for data analysis, including its advanced functions
K_U07 Can form an in-depth evaluation of the measures undertaken for the purpose of solving a social problem, based on knowledge and analytical skills acquired
K_U10 Can prepare a presentation of a selected problem or study in Polish and in a foreign language
K_K03 Can gather, find, synthesize and critically assess information about social sciences
K_K04 Can argue a thesis using scientific evidence
K_K09 Is open to various theoretical and methodological perspectives of social research
K_K10 Takes responsibility for planned and performed tasks
Kryteria oceniania
1. Oral exam (80%) consisting of 1 long, prepared answer (50%)
and 2 short questions (15% each) based on contents of
meetings, reading materials and prespecified list of topics.
2. Re-sit: oral exam consisting of 5 questions covering course
material (16% each = 80%).
3. Participation in discussions (20%).
4. Total: (1/2 + 3) 100%.
5. Two permissible absences.
Grades:
51% - 60% - 3
61% - 70% - 3.5
71% - 80% - 4
81% - 90% - 4.5
91% - 100% - 5
Literatura
Reading examples:
1. Wasserman, S., Faust, K. (1994). Social network analysis: Methods and applications. The Press Syndicate of the University of Cambridge.
2. White, H.C. (2008). Identity and control. How social formations emerge. Princeton University Press.
3. Centola, D., & Macy, M. (2007). Complex contagions and the weakness of long ties. American Journal of Sociology, 113(3), 702-734.
4. Borgatti, S. P., Carley, K. M., & Krackhardt, D. (2006). On the robustness of centrality measures under conditions of imperfect data. Social networks, 28(2), 124-136.
5. Albert, R., & Barabási, A. L. (2002). Statistical mechanics of complex networks. Reviews of modern physics, 74(1), 47.
6. Lazega, E. (2001). The collegial phenomenon: The social mechanisms of cooperation among peers in a corporate law partnership. Oxford University Press on Demand.
7. Padgett, J. F., & McLean, P. D. (2006). Organizational invention and elite transformation: The birth of partnership systems in Renaissance Florence. American journal of Sociology, 111(5), 1463-1568.
8. Krackhardt, D. (1990). Assessing the political landscape: Structure, cognition, and power in organizations. Administrative science quarterly, 342-369.
9. Burt, R. S. (2007). Brokerage and closure: An introduction to social capital. OUP Oxford.
10. Granovetter, M. (1983). The strength of weak ties: A network theory revisited. Sociological theory, 201-233.
11. Hummon, N. P., & Doreian, P. (2003). Some dynamics of social balance processes: bringing Heider back into balance theory. Social networks, 25(1), 17-49.
12. Lusher, D., Koskinen, J., & Robins, G. (Eds.). (2013). Exponential random graph models for social networks: Theory, methods, and applications. Cambridge University Press.
13. Snijders, T. A., Van de Bunt, G. G., & Steglich, C. E. (2010). Introduction to stochastic actor-based models for network dynamics. Social networks, 32(1), 44-60.
14. Butts, C. T., Lomi, A., Snijders, T. A., & Stadtfeld, C. (2023). Relational event models in network science. Network Science, 11(2), 175-183.
15. Valente, T. W. (2012). Network interventions. Science, 337(6090), 49-53.
16. Laumann, E. O., & Youm, Y. (1999). Racial/ethnic group differences in the prevalence of sexually transmitted diseases in the United States: a network explanation. Sexually transmitted diseases, 26(5), 250-261.
17. Carley, K. M., Diesner, J., Reminga, J., & Tsvetovat, M. (2007). Toward an interoperable dynamic network analysis toolkit. Decision Support Systems, 43(4), 1324-1347.
18. Fuhse, J., Stuhler, O., Riebling, J., & Martin, J. L. (2020). Relating social and symbolic relations in quantitative text analysis. A study of parliamentary discourse in the Weimar Republic. Poetics, 78, 101363.
19. Wellman, B., & Wortley, S. (1990). Different strokes from different folks: Community ties and social support. American journal of Sociology, 96(3), 558-588.
20. Bidart, C., & Lavenu, D. (2005). Evolutions of personal networks and life events. Social networks, 27(4), 359-376.
21. Roberts, S. G., & Dunbar, R. I. (2011). The costs of family and friends: an 18-month longitudinal study of relationship maintenance and decay. Evolution and Human Behavior, 32(3), 186-197.
22. Holme, P., & Saramäki, J. (2012). Temporal networks. Physics reports, 519(3), 97-125.
23. Documentation of R/Python packages (e.g. xUCINET, RSiena, ERGM, BERGM, networkX, igraph).
Więcej informacji
Dodatkowe informacje (np. o kalendarzu rejestracji, prowadzących zajęcia, lokalizacji i terminach zajęć) mogą być dostępne w serwisie USOSweb: