MATH-602 / 3 credits

Teacher(s): Abbé Emmanuel, Berthier Raphaël Jean

Language: English

Remark: Next time in 2024/25


Every year


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.



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.



Notes on "Random graphs" and monograph on "Community detection and stochastic block models" by E. Abbe. List of papers.

Ressources en bibliothèque

Moodle Link

In the programs

  • Exam form: Oral (session free)
  • Subject examined: Inference on graphs
  • Lecture: 20 Hour(s)
  • Practical work: 40 Hour(s)

Reference week

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