%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 }