Inferring hierarchical structure of spatial and generic complex networks through a modeling framework

Our recent paper [Grauwin et al. Sci. Rep. 7 (2017)] demonstrates that community and hierarchical structure of the networks of human interactions largely determines the least and should be taken into account while modeling them. In the present proof-of-concept pre-print the opposite question is considered: could the hierarchical structure itself be inferred to be best aligned with the network model? The inference mechanism is provided for both spatial networks as well as complex networks in general through a model based on hierarchical and (if defined) geographical distances. The mechanism allows to discover hierarchical and community structure at any desired resolution in complex networks and in particular the space-independent structure of the spatial networks. The approach is illustrated on the example of the interstate people migration network in USA.