Network Science
A.Y. 2023/2024
Learning objectives
The learning objective of the course is provide students with the main concepts, methods and algorithms of network science.
Expected learning outcomes
At the end of the course students will be able to design and carry out large-scale network studies.
Lesson period: First trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
First trimester
Online lectures
Course syllabus
Connectivity of networks
Network models
Centrality measures
Scale-free networks
Triadic closure and clustering coefficient
Small-world effect
Node similarity
Node assortativity
Community detection
Information diffusion
Machine learning on networks
Network models
Centrality measures
Scale-free networks
Triadic closure and clustering coefficient
Small-world effect
Node similarity
Node assortativity
Community detection
Information diffusion
Machine learning on networks
Prerequisites for admission
The course requires knowledge of basic computer science principles and familiarity with linear algebra and statistics.
Teaching methods
Lectures.
Teaching Resources
Slides and materials are posted to the website of the course.
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.
INF/01 - INFORMATICS - University credits: 6
Lessons: 40 hours
Professors:
Gaito Sabrina Tiziana, Zignani Matteo
Educational website(s)
Professor(s)
Reception:
by appointment via email
office (Celoria 18, floor VII) or online (covid emergency)