Network science
A.A. 2023/2024
Obiettivi formativi
The learning objective of the course is provide students with the main concepts, methods and algorithms of network science.
Risultati apprendimento attesi
At the end of the course students will be able to design and carry out large-scale network studies.
Periodo: Primo trimestre
Modalità di valutazione: Esame
Giudizio di valutazione: voto verbalizzato in trentesimi
Corso singolo
Questo insegnamento non può essere seguito come corso singolo. Puoi trovare gli insegnamenti disponibili consultando il catalogo corsi singoli.
Programma e organizzazione didattica
Edizione unica
Responsabile
Periodo
Primo trimestre
Online lectures
Programma
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
Prerequisiti
The course requires knowledge of basic computer science principles and familiarity with linear algebra and statistics.
Metodi didattici
Lectures.
Materiale di riferimento
Slides and materials are posted to the website of the course.
Modalità di verifica dell’apprendimento e criteri di valutazione
The exam consists of an oral discussion on the theoretical topics of the course and the presentation of a project. 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.
Siti didattici
Docente/i
Ricevimento:
su appuntamento via email (by appointment via email)
ufficio (Celoria 18, VII piano) o online (emergenza covid)