|SCIENTIFIC DISCIPLINARY SECTOR||INF/01|
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.
MARINA RIBAUDO (President)
GIOVANNA GUERRINI (Substitute)
All class schedules are posted on the EasyAcademy portal.
Oral examination with discussion of the practicals assigned during the course.