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, diligent students who have worked as instructed will have:
To be successful in this course, students should have basic knowledge concerning:
Lectures, practicals, and individual study.
Ricevimento: By appointement at the DIBRIS Department, room 231, 2nd floor, Via Dodecaneso 25, Genova. Online on Teams in case of distance learning. E-mail: marina.ribaudo@unige.it
MARINA RIBAUDO (President)
MATTEO DELL'AMICO
GIOVANNA GUERRINI (Substitute)
Oral examination with discussion of the practicals assigned during the course.
Individual interview.