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Hybrid multiscale coarse-graining for dynamics on complex networks

Research paper by Chuansheng Shen, Hanshuang Chen, Zhonghuai Hou, Jürgen Kurths

Indexed on: 23 Dec '18Published on: 20 Dec '18Published in: Chaos (Woodbury, N.Y.)



Abstract

Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 28, Issue 12, December 2018. We propose a hybrid multiscale coarse-grained (HMCG) method which combines a fine Monte Carlo (MC) simulation on the part of nodes of interest with a more coarse Langevin dynamics on the rest part. We demonstrate the validity of our method by analyzing the equilibrium Ising model and the nonequilibrium susceptible-infected-susceptible model. It is found that HMCG not only works very well in reproducing the phase transitions and critical phenomena of the microscopic models, but also accelerates the evaluation of dynamics with significant computational savings compared to microscopic MC simulations directly for the whole networks. The proposed method is general and can be applied to a wide variety of networked systems just adopting appropriate microscopic simulation methods and coarse graining approaches.