Unit name | Complex Networks |
---|---|
Unit code | MATH36201 |
Credit points | 20 |
Level of study | H/6 |
Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
Unit director | Dr. Ayalvadi Ganesh |
Open unit status | Not open |
Pre-requisites |
MATH11300 Probability 1 (or equivalent) and MATH 11005 Linear Algebra & Geometry (or equivalent). MATH 21400 (Applied Probability 2) is strongly recommended. |
Co-requisites |
none |
School/department | School of Mathematics |
Faculty | Faculty of Science |
Unit aims
Understand how to mathematically model complex networks. Learn to analyse stochastic processes on networks.
Unit description
This unit will teach ways of modelling and working with large complex networks such as the Internet and social networks. The topics covered will be:
Relation to other units
The unit extends Markov chain models seen in Probability 2 to continuous time, and applies them to the study of random processes on networks. Information Theory, Complex Networks, Financial Mathematics, and Queueing Networks, all involve the application of probability theory to problems arising in various fields.
Probability 2 is a pre-requisite for this course. Students from other departments who have not taken it should discuss the suitability of this course with the unit organiser before registering for it.
Additional unit information can be found at http://www.maths.bristol.ac.uk/study/undergrad/current_units/index.html
Learning Objectives
Lectures and problem sheets, from which work will be set and marked, with outline solutions handed out a fortnight later.
100% Examination
Raw scores on the examinations will be determined according to the marking scheme written on the examination paper. The marking scheme, indicating the maximum score per question, is a guide to the relative weighting of the questions. Raw scores are moderated as described in the Undergraduate Handbook.
Reading and references are available at http://www.maths.bristol.ac.uk/study/undergrad/current_units/index.html