TEACHING/SUPERVISION

MSc Program in Computational Social Science

The Master’s Program in Computational Social Science (CSS) at the Institute for Analytical Sociology at Linköping University is the first Master of Science program in Computational Social Science in Europe that has run since 2017. During the program, students train to apply computational methods to analyze large, complex datasets related to human social behavior, and to arrive at theoretically and empirically grounded explanations of social outcomes such as ethnic segregation in schools, income inequality, firm growth and survival, political change and cultural diffusion.  In the process, students are inducted into multidisciplinary domains of research in the social sciences that connect sociology, political science, economics, management science, and related disciplines with technical innovations in mathematics, statistics, and computer science. I served as program director in 2022. The current director of the Master program is Benjamin Jarvis.

Social Network Analysis

Social Network Analysis is a 7.5 ECTS course in the second semester of the Master’s Program in Computational Social Science (CSS) at the Institute for Analytical Sociology at Linköping University with the participation of Christian Steglich, Carl Nordlund, and Alexandra Rottenkolber. The course typically runs from January till the end of March. The course intends to provide a simultaneous introduction to the theoretical and methodological aspects of social network analysis. After successfully completing the course, the students should be able to think in terms of networks and grasp elementary social network concepts; address social science questions considering social network embeddedness; store, read, describe, and visualize network data using the statistical environment R; detect communities and identify role patterns in networks; run exponential random graph models (ERGMs) on cross-sectional network data; and analyze longitudinal social network data, including selection and influence mechanisms using Stochastic Actor Oriented Models (SAOMs).

PhD students supervised or co-supervised

(with date of defense)

PhD students currently supervised or co-supervised

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