In the last years, Social Network Analysis (SNA) has become an active field of research in the statistical framework due the increasing demand of information on a society which is progressively getting more and more "connected". Interesting applications entail, for instance, social integration, import/export flows, migration, etc... The course aims at studying some of the most recent and advanced methodologies in the SNA field.
Textbooks will be suggested at the beginning of the course
The course deals with statistical methods and tools for the analysis of social network data. In the first part of the course, emphasis will be given to the study of descriptive tools of analysis. These entail both network visualization and the building of social indicators for the analyses of the structural importance and the cohesion of network's nodes. The second part of the course is devoted to the development of statistical models for social network data. At the end of the course, students will be able to (a) describe and summarize network characteristics; (b) describe and explain objectives and assumptions underlying the different techniques addressed during the course; (c) critically reflect on the methodological adequacy of the different approaches studied during the course; (d) effectively disseminate results of the analysis.
Basic knowledge of probability, statistics, and regression modeling
Theoretical classes and lab activities
Type of Assessment
The exam consists of a project work and an oral discussion. These aims at assessing: (i) knowledge of the course topics; (ii) the ability to identify appropriate methods of analysis based on the available data and the research objectives; (iii) draw adequate conclusions on the bases of the results of analysis; (iv) communication skills.
Project work (30%): analysis of a dataset via the methodologies introduced during the course
Oral discussion (70%): 3 questions. Duration: up to a max of 60 minutes.
- Introduction to social network data
- Network representation: types of relations, graph representation, matrix representation
- Network visualization
- Descriptive analysis of network data: network statistics
- Descriptive analysis of network data: nodal statistics
- Exponential Random Graph models
- Stochastic blockmodels