Code
IGFE CSC 7282
Domain
Informatique
Program
Master
Language
Anglais/English
ECTS Credits
4
Class hours
30
Workload
45
Program Manager(s)
Department
- Réseaux et Services de Télécom
Educational team
Organisation
Cours/TD/TP/projet/examen : Class=15h/Labs=15hLearning objectives
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
Prerequisites
Algorithmic, Basic ML, Statistics, Python
Keywords
Graph,Complex Networks, ML
Evaluation
final grade: final projet
References
[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.