Scheda programma d'esame
Social network analysis
Academic year2020/21
PeriodSemester 2

Learning outcomes

Over the past decade there has been a growing public fascination with the complex “connectedness” of modern society. This connectedness is found in many contexts: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place, and in the ability of news and information as well as epidemics and financial crises to spread around the world with surprising speed and intensity. These are phenomena that involve networks and the aggregate behavior of groups of people; they are based on the links that connect us and the ways in which each of our decisions can have subtle consequences for the outcomes of everyone else.

This course is an introduction to the analysis of complex networks, with a special focus on social networks and the Web - their structure and function, and how it can be exploited to search for information. Drawing on ideas from computing and information science, applied mathematics, economics and sociology, the course describes the emerging field of study that is growing at the interface of all these areas, addressing fundamental questions about how the social, economic, and technological worlds are connected.

Assessment criteria of knowledge

- In the written exam, the student must demonstrate his/her knowledge of the course topics 

- In the oral exam the student must demonstrate the knowledge of the theory and must discuss the network analysis project.


Data-driven analysis of complex networks using a variety of models and software tools.

Assessment criteria of skills

The student must realize a project on network analysis.


The student will also acquire and / or develop appropriate sensitivity in the choices for the design and set-up of an analytical process. Finally, the student will learn how to interpret analytical results and how to present them properly.


Assessment criteria of behaviors

During the exam, the project choices made by the student and the ability to process network data will be evaluated. In addition, the accuracy and precision applied  in the design activities will be evaluated.


Teaching methods

Delivery: face to face

Learning activities:

  • attending lectures
  • preparation of oral/written report
  • individual study
  • Laboratory work

Attendance: Advised

Teaching methods:

  • Lectures
  • Task-based learning/problem-based learning/inquiry-based learning
  • project work

• Big graph data and social, information, biological and technological networks

• The architecture of complexity and how real networks differ from random networks: node degree and long tails, social distance and small worlds, clustering and triadic closure. Comparing real networks and random graphs. The main models of network science: small world and preferential attachment.

• Strong and weak ties, community structure and long-range bridges. Robustness of networks to failures and attacks. Cascades and spreading. Network models for diffusion and epidemics. The strength of weak ties for the diffusion of information. The strength of strong ties for the diffusion of innovation.

• Practical network analytics with Cytoscape and Gephi. Simulation of network processes with NetLogo.




David Easley, Jon Kleinberg: Networks, Crowds, and Markets. [[]]

Albert-Laszlo Barabasi. Network Science Book Project (2013, ongoing) [[]]



M. E. J. Newman: The structure and function of complex networks, SIAM Review, Vol. 45, p. 167-256, 2003. 

A.-L. Barabasi. Linked. PLUME, Penguin Group, 2002.


Software and data sources

Visual Analytics: 


Python (install the 2.7 version not the 3.x)


Data Collection:

Network Data Repository:

Assessment methods

Written exam, network analysis project and oral exam.

Updated: 09/02/2021 17:39