Social Network Analysis

A.Y. 2019/2020
Lesson for
6
Max ECTS
40
Overall hours
SSD
INF/01
Language
English
Learning objectives
The learning objective of the course is provide students with the main concepts and methods of social network analysis.

Students will learn to manage data about network structure and to analyze, model and visualize such data 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.

To achieve the above mentioned objectives, the course will consist of two units: i) "Web algorithms" (20 hours - 3 CFU), and ii) "Social network mining" (20 hours - 3 CFU).

Course structure and Syllabus

Active edition
Yes
Responsible
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
ATTENDING STUDENTS
Unit 1
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.
Unit 2
Syllabus
Networks models
Node degree
Clustering coefficient
Small-world effect
Node Similarity
Assortativity
Community detection
NON-ATTENDING STUDENTS
Unit 1
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.
Unit 2
Syllabus
Networks models
Node degree
Clustering coefficient
Small-world effect
Node Similarity
Assortativity
Community detection
Lesson period
First trimester
Lesson period
First trimester
Assessment methods
Esame
Assessment result
voto verbalizzato in trentesimi