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.
Lesson period: Second semester
(In case of multiple editions, please check the period, as it may vary)
The course presents fundamental concepts, measures, and effective algorithms for social network analysis and mining
Theory: Networks models Connected componentes Node degree Scale-free networks Clustering coefficient Small-world networks Node Similarity Assortativity Community detection Information diffusion Link prediction Lab: 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.
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.