Descriptif
The course will present the main properties of real graphs and some key algorithms for sampling, ranking and clustering nodes.You will learn how real graphs are structured, with a focus on the scale-free and small-world properties.
You will also learn how to find the most important nodes in the graph and how to detect communities, that is groups of nodes that are more densely connected.
A large part of the course will be devoted to programming in Python where you will have to implement various algorithms for analysing real datasets.
24 heures en présentiel (16 blocs ou créneaux)
Diplôme(s) concerné(s)
Parcours de rattachement
Pour les étudiants du diplôme Diplôme d'ingénieur
Students are supposed to have previously acquired basic knowledge in graph algorithms (search, shortest paths), probability (as in INF101 and MDI104 first year courses), and Python programming.
Format des notes
Numérique sur 20Littérale/grade européenPour les étudiants du diplôme Diplôme d'ingénieur
L'UE est acquise si Note finale >= 10- Crédits ECTS acquis : 2.5 ECTS
- Crédit d'UE électives acquis : 2.5
La note obtenue rentre dans le calcul de votre GPA.
Pour les étudiants du diplôme Echange non diplomant
L'UE est acquise si Note finale >= 10- Crédits ECTS acquis : 2.5 ECTS
La note obtenue rentre dans le calcul de votre GPA.
Programme détaillé
* Sampling nodes and edges* Scale-free property
* Random graphs
* Small-world property
* Betweenness centrality
* Ranking
* Community detection
