PRESENTAZIONE

Le reti sono ovunque e possono essere rappresentate come grafi.  Questo insegnamento svelerà i pattern nascosti e le dinamiche delle reti in vari domini come i social network, internet e il web, i sistemi biologici e altro ancora. Gli studenti impareranno le basi teoriche dell'analisi delle reti e sperimenteranno, attraverso attività pratiche, ciò che è stato introdotto durante le lezioni.

OBIETTIVI E CONTENUTI

OBIETTIVI FORMATIVI

Learning algorithms and techniques for large scale graph analytics, including centrality measures, connected components, graph clustering, graph properties for random, small-world, and scale free graphs, graph metrics for robustness and resiliency, and graph algorithms for reference problems.

OBIETTIVI FORMATIVI (DETTAGLIO) E RISULTATI DI APPRENDIMENTO

At the end of the course, students will be able to:

EXPLAIN universal properties of graphs that can be used to study large networks, regardless of their application domains
​EXPLAIN popular ranking algorithms
UNDERSTAND which synthetic model represents best a real network
DISCUSS the evolution of large networks in the presence of failures or contagions
USE available libraries to IMPLEMENT exercises and put into practice the topics seen during lectures

PREREQUISITI

To be successful in this course, students should have knowledge on:

• Basic graph theory (definitions, paths, components, visits)
• Web (how it works, its structure)
• Programming (for the hands-on activities)

MODALITA' DIDATTICHE

Lectures, hands-on activities, and individual study.

PROGRAMMA/CONTENUTO

Students will learn how to analyze graphs of large size, even when it is impossible to visualize them because they are too big. Topics covered during the course are the following:

• Background on linear algebra and probability
• Complex networks introduction: examples from biology, sociology, economy, computer science
• Network topology (at local and global level): degree, centrality measures, connectivity, communities, and more
• Network models: random graphs, small-world, scale-free networks
• Web graph: Markov chains and random walk, ranking, search engines
• Robustness and fault tolerance of networks (random failures and target attacks)
• Dynamic evolution of networks (social contagion and epidemic spreading)
• Networks visualization using open source software tools

TESTI/BIBLIOGRAFIA

• F. Menczer, S. Fortunato, C. A. Davis: A First Course in Network Science, Cambridge University Press, 2020
• D. Easley and J. Kleinberg: Networks, Crowds, and Markets: Reasoning About a Highly Connected World (http://www.cs.cornell.edu/home/kleinber/networks-book/)
• A. Barabasi: Network Science (http://barabasilab.neu.edu/networksciencebook/)
• Scientific papers will be suggested during the course

DOCENTI E COMMISSIONI

Commissione d'esame

MARINA RIBAUDO (Presidente)

MATTEO DELL'AMICO

GIOVANNA GUERRINI (Supplente)

LEZIONI

INIZIO LEZIONI

See the official calendar of the MSc in Computer Science. The schedule for all the courses can be found on EasyAcademy.

Orari delle lezioni

L'orario di questo insegnamento è consultabile all'indirizzo: Portale EasyAcademy

ESAMI

MODALITA' D'ESAME

Oral examination with discussion of the:

• practical exercises assigned during the course
• theory introduced during lectures

MODALITA' DI ACCERTAMENTO

During the oral exam students will be evaluated based on:

• the quality of the produced code and the completeness of the reports
• their understanding of the theoretical concepts covered in the course
• their presentation skills

Calendario appelli