Serrano Moral, Ma. Ángeles (María Ángeles)Krioukov, DmitriBoguñá, Marián2010-07-052010-07-0520080031-9007https://hdl.handle.net/2445/13287We 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.4 p.application/pdfeng(c) American Physical Society, 2008Física estadísticaMecànica estadísticaStatistical physicsStatistical mechanicsinfo:eu-repo/semantics/article557916info:eu-repo/semantics/openAccess