Descriptif
The course will present the main properties of real graphs and some key algorithms for sampling, ranking, classifying, representing and clustering nodes.
You will also 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, how to detect clusters of nodes and how to classify nodes or predict new links.
A large part of the course will be devoted to programming in Python where you will have to implement and test various algorithms for analysing real datasets.
Objectifs pédagogiques
The objective is to be able to identify graph structures in data and to apply appropriate techniques for various learning tasks (prediction, ranking, clustering).
- Leçon : 21
- Contrôle de connaissance : 2
effectifs minimal / maximal:
20/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, and Python programming.
Pour les étudiants du diplôme Echange international non diplomant
Students are supposed to have previously acquired basic knowledge in graph algorithms (search, shortest paths), probability, and Python programming.
Format des notes
Numérique sur 20Littérale/grade européenPour les étudiants du diplôme Data & Artificial Intelligence
Vos modalités d'acquisition :
Exam + Quiz
Présence obligatoire aux TP.
L'UE est acquise si Note finale >= 10- Crédits ECTS acquis : 2.5 ECTS
La note obtenue rentre dans le calcul de votre GPA.
Pour les étudiants du diplôme Echange international non diplomant
Vos modalités d'acquisition :
Exam + Quiz
L'UE est acquise si Note finale >= 10- Crédits ECTS acquis : 2.5 ECTS
La note obtenue rentre dans le calcul de votre GPA.
Pour les étudiants du diplôme Diplôme d'ingénieur
Vos modalités d'acquisition :
Exam + Quiz
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.
Programme détaillé
* Sparse matrices
* Graph structure
* PageRank
* Clustering
* Hierarchical clustering
* Heat diffusion
* Spectral embedding
* Graph neural networks
