Through this course, participants will:
- become familiar with general notions of graph theory and network analysis
- learn how to model network data using R, to implement algorithms from the field of graph theory (e.g., community detection, small-wordness), and to use up-to-date tools from statistical network analysis (e.g., graphical Lasso, subset bootstrap, Bayesian modeling) to optimize network estimation and visualization
- understand the advantages, challenges, and limitations of network analysis in comparison to other analytical approaches
- and become able to critically assess papers dealing with network analysis and graph theory in psychological sciences.
Due to the COVID-19 crisis, the information in this section is particularly likely to change.Teaching and assessment will be delivered via a classroom setting, on the Louvain-la-Neuve campus, but could be carried out remotely (via Teams) should the health situation require to do so.
Due to the COVID-19 crisis, the information in this section is particularly likely to change.First session: Oral test (9 points /20 ) and oral presentation and discussion of a research paper (7 points / 20) + continuous assessment via homeworks (4 points / 20)
Second session: Oral test (20 points /20)