Students will be able to deal with methods and models for network analysis, and understand how they can be used on empirical network data. This will enable them to independently address research questions from various fields.
Knowledge and understanding: basic analytical concepts and tools to describe and model social network structure across various levels of analysis.
Applying knowledge and understanding: being capable of dealing with relational data, and to implement different approaches to network data analysis in R.
Communication skills: being able to explain the basic ideas and communicate the results to experts and to non-experts.
Learning skills: being capable to understand scientific literature on network analysis topics and to combine appropriate methods useful for the problem at hand.
Students are required to have basic knowledge in inferential statistics and should be familiar with linear and logistic regression models. Some basic knowledge of software R will be also required.
- Basic analytical concepts in network analysis
- Network data collection
- Network visualization
- Descriptive analysis and network indices
- Network decomposition
- Modeling network structure
The course will include practical examples and hands-on computer laboratories based on the analysis of real-life relational data. In the laboratories, the emphasis will be on the analysis of social networks in structured social and economic settings such as, for example, business companies, and organizations.
Frontal lectures and hands on computer laboratory sessions with the software R, both individual and in groups. The balance will be roughly 65% of frontal lectures and 35% of hands-on sessions.
The exam will consist in the presentation and discussion, in groups of 2 up to 4 students, on the analysis (description and model fitting) of a real network dataset, explaining the working steps and the obtained results. The writing of a short report is also requested, with the commented R code.
During the presentations, few questions will be asked to assess the individual contributions and preparation on the topics of the course.
1. Kolaczyk E.D. (2009) Statistical Analysis of Network Data. Methods and Models, Springer, New York.
2. Lusher D., Koskinen J. and Robins G. (eds.) (2013) Exponential random graph models for social networks: Theory, methods, and applications. Cambridge University Press (selected chapters).
3. Hanneman R.A. and Riddle M. (2005) Introduction to social network methods. Riverside, CA: University of California, Riverside (published in digital form at http://faculty.ucr.edu/~hanneman/).
4. Amati V., Lomi A. and Mira A. (2018) Social network modeling. Annual Review of Statistics and Its Application, 5, pp.343-369.
Additional materials, lecture notes and information will be available at the course web page and via moodle2 e-learning platform.