Literature DB >> 15600711

Vertex intrinsic fitness: how to produce arbitrary scale-free networks.

Vito D P Servedio1, Guido Caldarelli, Paolo Buttà.   

Abstract

We study a recent model of random networks based on the presence of an intrinsic character of the vertices called fitness. The vertex fitnesses are drawn from a given probability distribution density. The edges between pairs of vertices are drawn according to a linking probability function depending on the fitnesses of the two vertices involved. We study here different choices for the probability distribution densities and the linking functions. We find that, irrespective of the particular choices, the generation of scale-free networks is straightforward. We then derive the general conditions under which scale-free behavior appears. This model could then represent a possible explanation for the ubiquity and robustness of such structures.

Entities:  

Year:  2004        PMID: 15600711     DOI: 10.1103/PhysRevE.70.056126

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  6 in total

1.  Network growth models: A behavioural basis for attachment proportional to fitness.

Authors:  Michael Bell; Supun Perera; Mahendrarajah Piraveenan; Michiel Bliemer; Tanya Latty; Chris Reid
Journal:  Sci Rep       Date:  2017-02-13       Impact factor: 4.379

2.  Fitness preferential attachment as a driving mechanism in bitcoin transaction network.

Authors:  Ayana Aspembitova; Ling Feng; Valentin Melnikov; Lock Yue Chew
Journal:  PLoS One       Date:  2019-08-23       Impact factor: 3.240

3.  A dynamic power-law sexual network model of gonorrhoea outbreaks.

Authors:  Lilith K Whittles; Peter J White; Xavier Didelot
Journal:  PLoS Comput Biol       Date:  2019-03-08       Impact factor: 4.475

4.  Understanding network concepts in modules.

Authors:  Jun Dong; Steve Horvath
Journal:  BMC Syst Biol       Date:  2007-06-04

5.  Generalised thresholding of hidden variable network models with scale-free property.

Authors:  Sámuel G Balogh; Péter Pollner; Gergely Palla
Journal:  Sci Rep       Date:  2019-08-02       Impact factor: 4.379

6.  Generalised popularity-similarity optimisation model for growing hyperbolic networks beyond two dimensions.

Authors:  Bianka Kovács; Sámuel G Balogh; Gergely Palla
Journal:  Sci Rep       Date:  2022-01-19       Impact factor: 4.379

  6 in total

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