Social Network Analysis

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
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
First trimester
Prerequisites for admission
The course requires knowledge of basic computer science principles and familiarity with linear algebra and statistics.
Assessment methods and Criteria
The exam consists of an oral discussion. At the end of the oral exam, the overall evaluation is expressed in thirtieths, taking into account the following aspects: the degree of knowledge of the topics, the ability to apply knowledge to the resolution of concrete problems, the ability of critical reasoning.
Unit 1
Course syllabus
- Basic notions for networks from graph theory including distances and strongly connected components.
- Geometric centralities: closeness, harmonic centrality, betweenness.
- Spectral centralities: Landau-Berge (eigenvector) centrality, Seeley's index, Katz's index, PageRank.
- Basic organization of a large-scale, distributed web crawler.
- Consistent hashing methods for assigning work in distributed crawlers.
- Bloom filters.
Teaching methods
Teaching Resources
Slides and materials are posted to the website of the course.
Unit 2
Course syllabus
Networks models
Node degree
Clustering coefficient
Small-world effect
Node Similarity
Community detection
Teaching methods
Teaching Resources
Social Media Mining, Reza Zafarani, Mohammad Ali Abbasi, Huan Liu, Cambridge University Press, 2014
Unit 1
INF/01 - INFORMATICS - University credits: 3
Lessons: 20 hours
Professor: Vigna Sebastiano
Unit 2
INF/01 - INFORMATICS - University credits: 3
Lessons: 20 hours
by appointment via email
office (Celoria 18, floor VII) or online (covid emergency)