Theory and practice of social networks

A.Y. 2019/2020
Overall hours
Learning objectives
The learning objective of the course is provide students with the main concepts methods and algorithms of social network analysis.
Expected learning outcomes
Students will be able to apply the concepts, methods and models to analyze, model and visualize data from social networks to get valuable insights. At the end of the course students will be able to design and carry out large-scale social network analysis studies.
Course syllabus and organization

Single session

Lesson period
Second semester
Course syllabus
The course presents fundamental concepts, measures, and effective algorithms for social network analysis and mining

Networks models
Connected componentes
Node degree
Scale-free networks
Clustering coefficient
Small-world networks
Node Similarity
Community detection
Information diffusion
Link prediction
Data gathering: web scraping and social media API
Network visualization - Gephi
Network analysis - NetworkX
Prerequisites for admission
The course requires knowledge of basic computer science principles, sufficient to write a reasonably non-trivial computer program and familiarity with linear algebra and statistics.
Teaching methods
Lectures and in-class lab exercizes
Teaching Resources

Recommended texts:
Social Media Mining, Reza Zafarani, Mohammad Ali Abbasi, Huan Liu, Cambridge University Press, 2014
Albert-LászlóBarabási: "Network Science"
D. Easley, J. Kleinberg, "Networks, Crowds, and Markets: Reasoning About a Highly Connected World" per la trattazio
Assessment methods and Criteria
The exam consists of a written test, a laboratory project and an oral discussion.
In the two-hour written test, students are asked to solve some exercises and to summarize some of the topics presented in the course.
The laboratory project consists of a social media analysis project designed by the student.
The exam ends with an oral discussion, which focuses on the presentation of the laboratory project followed by a discussion on the salient elements of the project itself and on the applied methodologies.

At the end of the oral test, the overall evaluation is expressed in thirtieths, taking into account the following aspects: the degree of knowledge of the topics, ability to apply knowledge to the resolution of concrete problems, the ability of critical reasoning.
INF/01 - INFORMATICS - University credits: 12
Laboratories: 48 hours
Lessons: 72 hours
by appointment via email
office (Celoria 18, floor VII) or online (covid emergency)