Adopting some key ideas of the AdS/CFT correspondence, such as the geometrization of the RG formalism and having an AdS background spacetime, mappings of the 1D and 2D Ising model onto a network...Show moreAdopting some key ideas of the AdS/CFT correspondence, such as the geometrization of the RG formalism and having an AdS background spacetime, mappings of the 1D and 2D Ising model onto a network model were developed. The mappings primarily serve to engineer a 2D phase transition into a higher dimensional tree network and show what holographic properties are obtained by merely invoking some conceptual ’ingredients’ from the holographic duality. The networks were studied by MC simulation of the Ising model and subsequent construction. This thesis then further reports on efforts to describe the network ensemble seeded off the Ising model independently, by a(n) (exponential) random graph model.Show less
The availability of information about complex networks is severely restrained by issues, such as confidentiality and privacy. This poses a problem when analysing properties of networks that are of...Show moreThe availability of information about complex networks is severely restrained by issues, such as confidentiality and privacy. This poses a problem when analysing properties of networks that are of relevance to the general public. One example is the study of the resilience of banking networks to financial distress. We discuss a random graph model that reconstructs such unavailable networks, based on information that is either node specific or specific to groups of nodes. The procedure is determined by enforcing renormalizability, i.e. consistency when modelling renormalizations of networks. Then we propose a weighted semi-renormalizable extension of this model. Both models are tested on an empirical trade network, by analysing how well they captures properties that are commonly used to characterize networks. Their performances are shown to closely resemble that of the (weighted) fitness-induced Configuration Model.Show less
