2015
DOI: 10.1038/srep10073
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Emergent Complex Network Geometry

Abstract: Networks are mathematical structures that are universally used to describe a large variety of complex systems such as the brain or the Internet. Characterizing the geometrical properties of these networks has become increasingly relevant for routing problems, inference and data mining. In real growing networks, topological, structural and geometrical properties emerge spontaneously from their dynamical rules. Nevertheless we still miss a model in which networks develop an emergent complex geometry. Here we sho… Show more

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Cited by 124 publications
(145 citation statements)
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References 58 publications
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“…Scale bar in (g) 10 µm. i A schematic of non-equivalent paths, running in the interface space between CDW puddles, connecting point A to point B in the emergent complex hyperbolic geometry 29,30 , for the superconducting current.The broad phase transition appears to be arrested, as indicated by the size of the CDW puddles ξ a =1/Δh CDW , which does not diverge below T CDW . This behavior is typical of low dimensional systems with quenched disorder.…”
mentioning
confidence: 99%
See 1 more Smart Citation
“…Scale bar in (g) 10 µm. i A schematic of non-equivalent paths, running in the interface space between CDW puddles, connecting point A to point B in the emergent complex hyperbolic geometry 29,30 , for the superconducting current.The broad phase transition appears to be arrested, as indicated by the size of the CDW puddles ξ a =1/Δh CDW , which does not diverge below T CDW . This behavior is typical of low dimensional systems with quenched disorder.…”
mentioning
confidence: 99%
“…Scale bar in (g) 10 µm. i A schematic of non-equivalent paths, running in the interface space between CDW puddles, connecting point A to point B in the emergent complex hyperbolic geometry 29,30 , for the superconducting current.…”
mentioning
confidence: 99%
“…Remarkably, widely different complex systems, across diverse natural and social domains, tend to display a common set of structural similarities [124,125,126,127,128]. These include a high clustering coefficient (the formation of communities within a network), short path lengths (most nodes can be reached by a path that travels though only a small number of other nodes), and power-law or scale-free degree distributions (degree is the number of connections at a node).…”
Section: Criticality Indicatorsmentioning
confidence: 99%
“…More recent "geometric" approaches consider discrete definitions of curvature as in [13,[16][17][18].…”
Section: Introductionmentioning
confidence: 99%
“…While the body of theoretical research on the analysis of networks and related structures has been focused on the properties of the various (discrete) Laplacians (see [10] for an overview on the state of the art), a more recent and related direction concerns the geometrical characterization of real-world and model type networks with discrete curvature, see [11][12][13][14].…”
Section: Introductionmentioning
confidence: 99%