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Theory Of Complex Networks

Key information

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Module description


Microscopic properties of networks: adjacency matrix, vertex degree, clustering coefficient, measures of node centrality and node similarity.   Macroscopic properties of networks: degree distributions, graph modularity, and assortativity.   Processes on networks: voter model, diffusion process, random walk on a graph, PageRank, and spectral distribution.   Random graphs: Erdos-Renyi ensemble, graphs with a prescribed degree distribution, giant components and percolation transition.  


Good knowledge of multivariate calculus, linear algebra and probability concepts

Assessment details

Written examination.

Semester 1 only students will be set an alternative assessment in lieu of in-person exams in January.

Full year students will complete the standard assessment.


Educational aims & objectives

Present the basic concepts of the theory of complex networks. Introduce various techniques which should enable the student to partake in active research in the field.

Teaching pattern

Two hours of lectures and one hour of tutorial per week throughout the term

Suggested reading list

Indicative reading list - link to Leganto system where you can search with module code for lists

Module description disclaimer

King’s College London reviews the modules offered on a regular basis to provide up-to-date, innovative and relevant programmes of study. Therefore, modules offered may change. We suggest you keep an eye on the course finder on our website for updates.

Please note that modules with a practical component will be capped due to educational requirements, which may mean that we cannot guarantee a place to all students who elect to study this module.

Please note that the module descriptions above are related to the current academic year and are subject to change.