Code
IGFE CSC 7282
Domaine
Informatique
Programme
Master
Langue
Anglais/English
Crédits ECTS
4
Heures programmées
30
Charge de travail
45
Coordonnateur(s)
Département
- Réseaux et Services de Télécom
Equipe pédagogique
Organisation
Cours/TD/TP/projet/examen : Class=15h/Labs=15hAcquis d'apprentissage
1. Introduction and overview
2. Graph Theory and network basics
3. centrality measures
4. Eigen centrality, PageRank
5. Recommendation engine
6. random graphs (simple)
7. configuration model
8. Advanced random graph model
9. Network resiliency
10. Spreading processes
11. Social Network Analysis
12. Fake news spreading process
13. Community detection on networks
14. data wrangling + data sampling
15. ML and Graph learning
16. Graph Convolutional Network and ML classification problems
Prérequis
Algorithmic, Basic ML, Statistics, Python
Mots-clés
Graph,Complex Networks, ML
Evaluation
final grade: final projet
Bibliographie
[1] D. Easley and J. Kleinberg, Reasoning about a Highly Connected World. Cambridge University Press, 2010.
[2] M. E. J. Newman, Networks : an introduction. Oxford University Press, 2010.
[3] J. Leskovec, A. Rajaraman & J. D. Ullman. Mining of massive datasets, Cambridge University Press, 2020.
[4] William L. Hamilton, Graph Representation Learning, Cambridge University Press, 2020.