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
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.
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