Inferring hierarchical structure of spatial and generic complex networks through a modeling framework
Abstract
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.
Cite
@article{arxiv.1712.05792,
title = {Inferring hierarchical structure of spatial and generic complex networks through a modeling framework},
author = {Stanislav Sobolevsky},
journal= {arXiv preprint arXiv:1712.05792},
year = {2017}
}
Comments
12 pages, 35 references, 4 figures