Networks are everywhere and they can be represented as graphs. This course will unravel the hidden patterns and dynamics of networks in various domains such as social networks, internet and the web, biological systems, and more. Students will learn the theoretical foundations of network analysis and experience hands-on activities to put into practice what introduced during classes.
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: By appointment (in person or online), taken via email.
MARINA RIBAUDO (President)
MATTEO DELL'AMICO
GIOVANNA GUERRINI (Substitute)
In agreement with the calendar approved by the Degree Program Board of Computer Science.
Oral examination with discussion of the:
During the oral exam students will be evaluated based on: