We present a general model for the growth of weighted networks in which the structural growth is coupled with the edges’ weight dynamical evolution. The model is based on a simple weight-driven dynamics and a weights’ reinforcement mechanism coupled to the local network growth. That coupling can be generalized in order to include the effect of additional randomness and nonlinearities which can be present in real-world networks. The model generates weighted graphs exhibiting the statistical properties observed in several real-world systems. In particular, the model yields a nontrivial time evolution of vertices’ properties and scale-free behavior with exponents depending on the microscopic parameters characterizing the coupling rules. Very interestingly, the generated graphs spontaneously achieve a complex hierarchical architecture characterized by clustering and connectivity correlations varying as a function of the vertices’ degree.
%0 Journal Article
%1 barrat_modeling_2004
%A Barrat, Alain
%A Barthélemy, Marc
%A Vespignani, Alessandro
%D 2004
%J Physical Review E
%K \_tablet\_modified
%N 6
%P 066149
%R 10.1103/PhysRevE.70.066149
%T Modeling the evolution of weighted networks
%U http://link.aps.org/doi/10.1103/PhysRevE.70.066149
%V 70
%X We present a general model for the growth of weighted networks in which the structural growth is coupled with the edges’ weight dynamical evolution. The model is based on a simple weight-driven dynamics and a weights’ reinforcement mechanism coupled to the local network growth. That coupling can be generalized in order to include the effect of additional randomness and nonlinearities which can be present in real-world networks. The model generates weighted graphs exhibiting the statistical properties observed in several real-world systems. In particular, the model yields a nontrivial time evolution of vertices’ properties and scale-free behavior with exponents depending on the microscopic parameters characterizing the coupling rules. Very interestingly, the generated graphs spontaneously achieve a complex hierarchical architecture characterized by clustering and connectivity correlations varying as a function of the vertices’ degree.
@article{barrat_modeling_2004,
abstract = {We present a general model for the growth of weighted networks in which the structural growth is coupled with the edges’ weight dynamical evolution. The model is based on a simple weight-driven dynamics and a weights’ reinforcement mechanism coupled to the local network growth. That coupling can be generalized in order to include the effect of additional randomness and nonlinearities which can be present in real-world networks. The model generates weighted graphs exhibiting the statistical properties observed in several real-world systems. In particular, the model yields a nontrivial time evolution of vertices’ properties and scale-free behavior with exponents depending on the microscopic parameters characterizing the coupling rules. Very interestingly, the generated graphs spontaneously achieve a complex hierarchical architecture characterized by clustering and connectivity correlations varying as a function of the vertices’ degree.},
added-at = {2017-01-09T13:57:26.000+0100},
author = {Barrat, Alain and Barthélemy, Marc and Vespignani, Alessandro},
biburl = {https://www.bibsonomy.org/bibtex/2054f8b62aaff45ddd0e3cf26ca590edc/yourwelcome},
doi = {10.1103/PhysRevE.70.066149},
interhash = {2ad6a9d1dd52779f5fb680af7ebab262},
intrahash = {054f8b62aaff45ddd0e3cf26ca590edc},
journal = {Physical Review E},
keywords = {\_tablet\_modified},
month = dec,
number = 6,
pages = 066149,
timestamp = {2017-01-09T14:01:11.000+0100},
title = {Modeling the evolution of weighted networks},
url = {http://link.aps.org/doi/10.1103/PhysRevE.70.066149},
urldate = {2014-07-19},
volume = 70,
year = 2004
}