We study a general set of models of social network evolution and dynamics. The models consist of both a dynamics on the network and evolution of the network. Links are formed preferentially between “similar” nodes, where the similarity is defined by the particular process taking place on the network. The interplay between the two processes produces phase transitions and hysteresis, as seen using numerical simulations for three specific processes. We obtain analytic results using mean-field approximations, and for a particular case we derive an exact solution for the network. In common with real-world social networks, we find coexistence of high and low connectivity phases and history dependence.
%0 Journal Article
%1 Ehrhardt2006
%A Ehrhardt, George C. M. A.
%A Marsili, Matteo
%A Vega-Redondo, Fernando
%D 2006
%J Phys. Rev. E
%K networks social-science adaptive-networks graphs
%P 036106
%R 10.1103/PhysRevE.74.036106
%T Phenomenological models of socioeconomic network dynamics
%V 74
%X We study a general set of models of social network evolution and dynamics. The models consist of both a dynamics on the network and evolution of the network. Links are formed preferentially between “similar” nodes, where the similarity is defined by the particular process taking place on the network. The interplay between the two processes produces phase transitions and hysteresis, as seen using numerical simulations for three specific processes. We obtain analytic results using mean-field approximations, and for a particular case we derive an exact solution for the network. In common with real-world social networks, we find coexistence of high and low connectivity phases and history dependence.
@article{Ehrhardt2006,
abstract = {We study a general set of models of social network evolution and dynamics. The models consist of both a dynamics on the network and evolution of the network. Links are formed preferentially between “similar” nodes, where the similarity is defined by the particular process taking place on the network. The interplay between the two processes produces phase transitions and hysteresis, as seen using numerical simulations for three specific processes. We obtain analytic results using mean-field approximations, and for a particular case we derive an exact solution for the network. In common with real-world social networks, we find coexistence of high and low connectivity phases and history dependence.},
added-at = {2011-01-13T13:25:48.000+0100},
author = {Ehrhardt, George C. M. A. and Marsili, Matteo and {Vega-Redondo}, Fernando},
biburl = {https://www.bibsonomy.org/bibtex/2d7ef9e7046f0cf1914dea4435ffa5843/rincedd},
doi = {10.1103/PhysRevE.74.036106},
file = {Ehrhardt2006 - Phenomenological models of socioeconomic network dynamics.pdf:Ehrhardt2006 - Phenomenological models of socioeconomic network dynamics.pdf:PDF},
interhash = {af16a5beaad0c3807d5e3a6fd40a0539},
intrahash = {d7ef9e7046f0cf1914dea4435ffa5843},
journal = {Phys. Rev. E},
keywords = {networks social-science adaptive-networks graphs},
pages = 036106,
timestamp = {2011-01-13T13:25:48.000+0100},
title = {Phenomenological models of socioeconomic network dynamics},
volume = 74,
year = 2006
}