Probing and shaping the information transfer of noise-perturbed complex networks via Markov Chain analysis

摘要

We demonstrate a recursive computational procedure based on the distributions of first passage time on Markov Chains that can mathematically characterize noise-driven processes in complex networks. Considering examples of both real (Enron email) and artificial (Ravasz-Barabasi) networks perturbed by noise using Monte Carlo simulations, our method accurately recovers the percentages that information will be transferred to the intended receivers. The paradigm reported here captures and provides explanation to the recent results of Czaplicka et al. (Nature Sci. Rep. 2013) showing that the presence of noise can actually enhance the transfer of information in a hierarchical complex network. Finally, we illustrate how adaptive thresholding guided by our developed procedure can be used to engineer or shape the dynamic range of networks operating in a noisy environment.