Network Dynamics
Nätverksdynamik

FRT095F, 9 credits

Valid from: Autumn 2013
Decided by: FN1/Anders Gustafsson
Date of establishment: 2014-02-07

General Information

Division: Automatic Control
Course type: Third-cycle course
Teaching language: English

Aim

This course will provide an introduction to and some analysis of the main mathematical models used to describe large networks and dynamical processes that evolve on networks. Motivation and applications will be drawn from social, economic, natural, and infrastructure networks, as well as networked decision systems such as sensor networks.

Goals

Knowledge and Understanding

For a passing grade the doctoral student must

• know the basic principles of graph theory and apply them to model real-world networks
• have insight in the basic differences between different models of random graphs
• be familiar with the properties of random walks on graphs
• be able to analyze simple dynamical systems over networks
• understand emerging phenomena in large-scale networks
• be able to give an overview of modern directions in network science

Competences and Skills

For a passing grade the doctoral student must

• be able to analyze properties of (random) graphs both quantitatively and qualitatively
• be able to handle basic analytical computations for random walks
• be able to analyze simple dynamical systems over networks and to relate their behavior to the network structure
• be able to practically apply the theory during a project
• be able to use computer tools for simulation and analysis of networks

Judgement and Approach

For a passing grade the doctoral student must

• be able to understand relations and limitations when simple models are used to describe complex networks
• be able to evaluate dominating emerging phenomena in network dynamics
• be able to read and critically examine an article from the current literature on the subject

Course Contents

Basic graph theory: connectivity, degree distributions, trees, adjacency matrices, spectrum. Random graphs: Erdos-Renyi, configuration model, preferential attachment, small-world, branching process approximations Flows and games on graphs: max-flow min-cut, optimal transport, Wardrop equilibria, evolutionary dynamics. Random walks on graphs: invariant distributions, hitting times, mixing times. Dynamical systems on graphs: distributed averaging, interacting particle systems, epidemics, opinion dynamics. Mean-field and branching process approximations.

Course Literature

TBD.
Lecture notes

Instruction Details

Types of instruction: Lectures, seminars, exercises, project

Examination Details

Examination formats: Written assignments, seminars given by participants
Grading scale: Failed, pass
Examiner:

Admission Details

Assumed prior knowledge: The course is designed for an audience of doctoral students with some mathematical background (in particular, probability and linear algebra at an intermediate level), possibly from the programs in Automatic Control, Mathematics, Mathematical Statistics, Physics, Computer Science, Economics, ...

Course Occasion Information

Contact and Other Information

Course coordinators: