Micro-scale foundation with error quantification for the approximation of dynamics on networks

Abstract

Mean-field approximations in complex networks, while useful, often vary in accuracy and can be particularly problematic in the case of dynamical correlations. Here, the authors report the use of approximate lumping to derive low-dimensional mean-field equations and estimate the overall approximation error for real-world networks.