Social Network Analysis
A.Y. 2022/2023
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.
Lesson period: First trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First trimester
Mainly synchronous online lectures
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
- Connectivity
- Network models
- Node centrality
- Node degree
- Network models
- Centrality measures
- Connectivity
- Network models
- Node centrality
- Node degree
- Network models
- Centrality measures
Teaching methods
Lectures
Teaching Resources
Slides and materials are posted to the website of the course.
Unit 2
Course syllabus
network models
Scale-free networks
Clustering coefficient
Small-world effect
Node Similarity
Assortativity
Community detection
Information diffusion
Gephi e NetworkX
Scale-free networks
Clustering coefficient
Small-world effect
Node Similarity
Assortativity
Community detection
Information diffusion
Gephi e NetworkX
Teaching methods
Lectures
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:
Gaito Sabrina Tiziana
Unit 2
INF/01 - INFORMATICS - University credits: 3
Lessons: 20 hours
Professor:
Gaito Sabrina Tiziana
Educational website(s)
Professor(s)
Reception:
by appointment via email
office (Celoria 18, floor VII) or online (covid emergency)