Scale-free networks with a large- to hypersmall-world transition
| dc.creator | Holme, Petter | |
| dc.date | 2006-07-05 | |
| dc.date | 2006-11-07 | |
| dc.date.accessioned | 2026-07-07T07:41:47Z | |
| dc.date.available | 2026-07-07T07:41:47Z | |
| dc.description | Recently there have been a tremendous interest in models of networks with a power-law distribution of degree -- so called "scale-free networks." It has been observed that such networks, normally, have extremely short path-lengths, scaling logarithmically or slower with system size. As en exotic and unintuitive example we propose a simple stochastic model capable of generating scale-free networks with linearly scaling distances. Furthermore, by tuning a parameter the model undergoes a phase transition to a regime with extremely short average distances, apparently slower than log log N (which we call a hypersmall-world regime). We characterize the degree-degree correlation and clustering properties of this class of networks. | |
| dc.description | errors fixed, one new figure, to appear in Physica A | |
| dc.identifier | https://arxiv.org/abs/cond-mat/0607111 | |
| dc.identifier | http://arxiv.org/abs/cond-mat/0607111 | |
| dc.identifier | Physica A 377, 315-322 (2007) | |
| dc.identifier | doi:10.1016/j.physa.2006.11.024 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/122241 | |
| dc.subject | Disordered Systems and Neural Networks | |
| dc.title | Scale-free networks with a large- to hypersmall-world transition | |
| dc.type | text |