Explaining the emergence of complex networks through log-normal fitness in a Euclidean node similarity space.

ABSTRACT: Networks of disparate phenomena-be it the global ecology, human social institutions, within the human brain, or in micro-scale protein interactions-exhibit broadly consistent architectural features. To explain this, we propose a new theory where link probability is modelled by a log-normal node fitness (surface) factor and a latent Euclidean space-embedded node similarity (depth) factor. Building on recurring trends in the literature, the theory asserts that links arise due to individualistic as well as dyadic information and that important dyadic information making up the so-called depth factor is obscured by this essentially non-dyadic information making up the surface factor. Modelling based on this theory considerably outperforms popular power-law fitness and hyperbolic geometry explan

SUBMITTER: Smith KM 

PROVIDER: S-EPMC7820353 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature