We review the main tools which allow for the statistical characterization of weighted networks. We then present two case studies, the airline connection network and the scientific collaboration network which are representatives of critical infrastructure and social system, respectively. The main empirical results are (i) the broad distributions of various quantities and (ii) the existence of weight-topology correlations. These measurements show that weights are relevant and that in general the modeling of complex networks must go beyond topology. We review a model which provides an explanation for the features observed in several real-world networks. This model of weighted network formation relies on the dynamical coupling between topology and weights, considering the rearrangement of new links are introduced in the system.
%0 Journal Article
%1 barthelemy_characterization_2005
%A Barthélemy, Marc
%A Barrat, Alain
%A Pastor-Satorras, Romualdo
%A Vespignani, Alessandro
%D 2005
%J Physica A: Statistical Mechanics and its Applications
%K networks networks, social weighted
%N 1–2
%P 34--43
%R 10.1016/j.physa.2004.08.047
%T Characterization and modeling of weighted networks
%U http://www.sciencedirect.com/science/article/pii/S0378437104011598
%V 346
%X We review the main tools which allow for the statistical characterization of weighted networks. We then present two case studies, the airline connection network and the scientific collaboration network which are representatives of critical infrastructure and social system, respectively. The main empirical results are (i) the broad distributions of various quantities and (ii) the existence of weight-topology correlations. These measurements show that weights are relevant and that in general the modeling of complex networks must go beyond topology. We review a model which provides an explanation for the features observed in several real-world networks. This model of weighted network formation relies on the dynamical coupling between topology and weights, considering the rearrangement of new links are introduced in the system.
@article{barthelemy_characterization_2005,
abstract = {We review the main tools which allow for the statistical characterization of weighted networks. We then present two case studies, the airline connection network and the scientific collaboration network which are representatives of critical infrastructure and social system, respectively. The main empirical results are (i) the broad distributions of various quantities and (ii) the existence of weight-topology correlations. These measurements show that weights are relevant and that in general the modeling of complex networks must go beyond topology. We review a model which provides an explanation for the features observed in several real-world networks. This model of weighted network formation relies on the dynamical coupling between topology and weights, considering the rearrangement of new links are introduced in the system.},
added-at = {2017-01-09T13:57:26.000+0100},
author = {Barthélemy, Marc and Barrat, Alain and Pastor-Satorras, Romualdo and Vespignani, Alessandro},
biburl = {https://www.bibsonomy.org/bibtex/2741fa2ef0437070d42a3f42c9c1485a0/yourwelcome},
doi = {10.1016/j.physa.2004.08.047},
interhash = {1eafb2104d35ad2462ae7d94243cfbb9},
intrahash = {741fa2ef0437070d42a3f42c9c1485a0},
issn = {0378-4371},
journal = {Physica A: Statistical Mechanics and its Applications},
keywords = {networks networks, social weighted},
month = feb,
number = {1–2},
pages = {34--43},
timestamp = {2017-01-09T14:01:11.000+0100},
title = {Characterization and modeling of weighted networks},
url = {http://www.sciencedirect.com/science/article/pii/S0378437104011598},
urldate = {2013-09-24},
volume = 346,
year = 2005
}