Self-similarity of complex networks and hidden metric spaces

dc.creatorSerrano, M. Angeles
dc.creatorKrioukov, Dmitri
dc.creatorBoguna, Marian
dc.date2007-10-10
dc.date2008-02-20
dc.date.accessioned2026-07-07T12:07:58Z
dc.date.available2026-07-07T12:07:58Z
dc.descriptionWe demonstrate that the self-similarity of some scale-free networks with respect to a simple degree-thresholding renormalization scheme finds a natural interpretation in the assumption that network nodes exist in hidden metric spaces. Clustering, i.e., cycles of length three, plays a crucial role in this framework as a topological reflection of the triangle inequality in the hidden geometry. We prove that a class of hidden variable models with underlying metric spaces are able to accurately reproduce the self-similarity properties that we measured in the real networks. Our findings indicate that hidden geometries underlying these real networks are a plausible explanation for their observed topologies and, in particular, for their self-similarity with respect to the degree-based renormalization.
dc.identifierhttps://arxiv.org/abs/0710.2092
dc.identifierhttp://arxiv.org/abs/0710.2092
dc.identifierPhysical Review Letters 100, 078701 (2008)
dc.identifierdoi:10.1103/PhysRevLett.100.078701
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/209158
dc.subjectDisordered Systems and Neural Networks
dc.subjectNetworking and Internet Architecture
dc.subjectPhysics and Society
dc.titleSelf-similarity of complex networks and hidden metric spaces
dc.typetext

Files

Collections