Inference on graphs
Frequency
Every year
Summary
The class covers topics related to statistical inference and algorithms on graphs: basic random graphs concepts, thresholds, subgraph containment (planted clique), connectivity, broadcasting on trees, stochastic block models and perceptron models. Requirement: basics of probability and statistics.
Content
The class covers topics related to statistical inference and algorithms on graphs: basic random graphs concepts, thresholds, subgraph containment (planted clique), connectivity, broadcasting on trees, stochastic block models and perceptron models. Requirement: basics of probability and statistics.
The class will have lectures and projects consisting of papers presentations, potential problem extensions and reports.
Keywords
Inference on graphs, learning on graphs, random graphs, community detecion, clustering, perceptron, neural networks, spectral graph theory.
Learning Prerequisites
Required courses
A basic class on probability and statistics
Learning Outcomes
- Understand the material of the class and related papers.
Resources
Bibliography
Notes on "Random graphs" and monograph on "Community detection and stochastic block models" by E. Abbe. List of papers.
Ressources en bibliothèque
In the programs
- Exam form: Oral (session free)
- Subject examined: Inference on graphs
- Lecture: 20 Hour(s)
- Practical work: 40 Hour(s)