The course requires knowledge of basic computer science principles and familiarity with linear algebra and statistics.
Assessment methods and Criteria
The exam consists of an oral discussion. At the end of the oral exam, the overall evaluation is expressed in thirtieths, taking into account the following aspects: the degree of knowledge of the topics, the ability to apply knowledge to the resolution of concrete problems, the ability of critical reasoning.
- Basic notions for networks from graph theory including distances and strongly connected components. - Geometric centralities: closeness, harmonic centrality, betweenness. - Spectral centralities: Landau-Berge (eigenvector) centrality, Seeley's index, Katz's index, PageRank. - Basic organization of a large-scale, distributed web crawler. - Consistent hashing methods for assigning work in distributed crawlers. - Bloom filters.
Slides and materials are posted to the website of the course.