High-accuracy approximation of binary-state dynamics on networks
Physics and Society
2015-03-19 v3 Disordered Systems and Neural Networks
Abstract
Binary-state dynamics (such as the susceptible-infected-susceptible (SIS) model of disease spread, or Glauber spin dynamics) on random networks are accurately approximated using master equations. Standard mean-field and pairwise theories are shown to result from seeking approximate solutions of the master equations. Applications to the calculation of SIS epidemic thresholds and critical points of non-equilibrium spin models are also demonstrated.
Cite
@article{arxiv.1104.1537,
title = {High-accuracy approximation of binary-state dynamics on networks},
author = {James P. Gleeson},
journal= {arXiv preprint arXiv:1104.1537},
year = {2015}
}
Comments
9 pages, 3 figures. This version accepted for publication in Physical Review Letters (with additional information in Appendices)