Networks out of control

Summary

The goal of this class is to acquire mathematical tools and engineering insight about networks whose structure is random, as well as learning and control techniques applicable to such network data.

Content

Keywords

Random graphs, network data, machine learning, graph processes.

Learning Prerequisites

Required courses

COM-300 Modèles stochastiques pour les communications, or equivalent

Important concepts to start the course

Basic probability and statistics; Markov chains; basic combinatorics.

Learning Outcomes

By the end of the course, the student must be able to:

• Develop models of networks driven by data or applications
• Analyze properties of random graphs
• Design algorithms dealing with random networks

Teaching methods

Ex cathedra lectures, exercises, mini-project

Assessment methods

• Homeworks 10%
• Mini-project 40%
• Final exam 50%

Resources

Bibliography

• A. D. Barbour, L. Holst and S. Janson, Poisson Approximation, Oxford Science Publications, 1992.
• B. Bollobas, Random Graphs (2nd edition), Cambridge University Press, 2001.
• R. Durrett, Random Graph Dynamics, Cambridge University Press, 2006 (electronic version).
• D. Easley, J. Kleinberg. Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Cambridge University Press, 2010 (electronic version).
• G. Grimmett, Percolation (2nd edition), Springer, 1999.
• S. Janson, T. Luczak, A. Rucinski, Random Graphs, Wiley, 2000.
• R. Meester and R. Roy, Continuum Percolation, Cambridge University Press, 1996.

Ressources en bibliothèque

• Poisson Approximation / Barbour
• Random Graphs / Bollobas
• Random Graph Dynamics / Durrett

Moodle Link

• https://go.epfl.ch/COM-512