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.
Making judgements: being able to select suitable methods for comprehending the properties of real-world networks.

Students are required to have basic knowledge of 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.

1. Kolaczyk E.D. (2009) Statistical Analysis of Network Data. Methods and
Models, Springer, New York (selected chapters).
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).
Recommended readings:
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.

.

Frontal lectures and hands-on computer laboratory sessions with the
software R, both individually and in groups. The balance will be roughly
65% of frontal lectures and 35% of hands-on sessions.

The exam will consist of the presentation and discussion, individually or 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, a few questions will be asked to assess the
individual contributions and preparation on the course topics. To obtain the minimum passing grade (18-30 points scale), students must address acceptably the report tasks.

The present course contains topics related to some goals or the UN Agenda 2030 for Sustainable Development.