Insegnamento a.a. 2025-2026

• Social network theories, concepts, and terminology (e.g., structural holes, social capital, social influence, origins and evolutions of network structures).
• Using matrices and graphs to represent social relationships (e.g., one-mode and two-mode networks, layout algorithms, network visualizations).
• Methods and measures to understand network data (e.g., centrality algorithms, cliques and communities, positions and roles, scale-free networks).
• Applications of social network analysis (e.g., professional networking, strategic alliances, organizational change, key-player detection).

• Explain the most important social network theories and identify their application to practical managerial problems and contexts.
• Recall the main terminology and define concepts associated with the analysis of social networks.
• Illustrate the main social network measures and statistical techniques that can be used to analyze relational data.
• Build effective professional networking that can help advancing your career
• Contrast different ways of visualizing social networks and illustrate the implications of their use.
• Articulate the strengths and limitations of the social network approach.

• Apply social network concepts to aid practical managerial decisions.
• Examine a business situation through a social network perspective to determine management needs.
• Improve their ability to establish and maintain effective professional networks.
• Design social network surveys to collect and analyze relational data.
• Employ statistical techniques and social network software to calculate different social network measures.
• Create detailed social network reports to communicate results in an effective way, including compelling and powerful network visualizations.

• Lectures
• Guest speaker's talks (in class or in distance)
• Practical Exercises
• Individual works / Assignments
• Collaborative Works / Assignments
• Interaction/Gamification

This course combines several complementary learning methods to create an engaging and applied experience.

• Lectures introduce the main theories and concepts in social network analysis, along with the technical foundations for collecting and analyzing relational data. These sessions are interactive: you’ll work through case studies, brief exercises, and in-class activities designed to make network thinking tangible and intuitive.
• Lab sessions translate ideas into practice. Using real data and your own laptop, you’ll learn how to design network studies and run analyses step by step. We’ll use either R or UCINET, two of the standard software packages for social network analysis. Please install the latest version before class: (https://sites.google.com/site/ucinetsoftware/home).
• Group project will give you a chance to step into the role of a network consultant. Working in teams, you’ll analyze and visualize network data, then present your findings to your peers and instructor. This project simulates the real-world challenges of translating analytical insights into clear, actionable stories for different audiences.

Class attendance is strongly encouraged, as many sessions involve interactive discussions and hands-on exercises that build directly on the readings and lectures.

Students will be evaluated on the following criteria:

Final written exam (100%): The exam is the same for all students, whether attending or non-attending. It includes a mix of open and closed questions designed to assess your understanding of core theories, key concepts, and terminology in social network analysis, as well as your ability to interpret and apply network data and methods using standard analytical tools.

Attending students can earn up to 2 additional points (on top of the exam grade) through the group final project. Working in teams, students will apply network analysis concepts and methods to real data and present their findings to the class. The project is an opportunity to demonstrate both analytical and communication skills in a practical setting. Finally, note that active participation during lectures and labs is highly encouraged, as it enhances learning and supports successful project work.

Lecture slides.

Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing Social Networks (2nd edition). SAGE Publications Limited.

Recommended: Hanneman, R. A., & Riddle, M. (2005). Introduction to Social Network Methods. Available online free of charge at http://faculty.ucr.edu/~hanneman/nettext/.

In addition to lectures, the course has also some lab-exercise sessions. Problem sets and their solutions will be posted on the online platform of the course.