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.
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.
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
To be successful in this course, students should have knowledge on:
Lectures, hands-on activities, and individual study.
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:
Ricevimento: Su appuntamento (in presenza o online) definito per email.
MARINA RIBAUDO (Presidente)
MATTEO DELL'AMICO
GIOVANNA GUERRINI (Supplente)
See the official calendar of the MSc in Computer Science. The schedule for all the courses can be found on EasyAcademy.
Oral examination with discussion of the:
During the oral exam students will be evaluated based on: