(i) models of networks (random graphs, small-world networks, scale-free networks, network ensembles),
(ii) characterisation of networks (degree distribution, centrality measures, k-core, community detection),
(iii) models on networks (Ising model, optimisation over networks, epidemic spreading models),
(iv) inference of networks (correlation networks, Gaussian networks, inverse statistical physics).
The focus of the course will be to give a solid and coherent methodological and theoretical basis, which can be used across a broad range of applications.
- Networks: An Introduction, M. Newman, Oxford University Press.
- Phase Transitions in Combinatorial Optimization Problems: Basics, Algorithms and Statistical Mechanics, A. K. Hartmann & M. Weigt, Wiley-VCH.
Martin Weigt (UPMC)
Comments are closed