Detecting the topologies of complex networks with stochastic perturbations

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Original languageEnglish
Article number43129
Journal / PublicationChaos
Volume21
Issue number4
Publication statusPublished - Dec 2011

Abstract

How to recover the underlying connection topology of a complex network from observed time series of a component variable of each node subject to random perturbations is studied. A new technique termed Piecewise Granger Causality is proposed. The validity of the new approach is illustrated with two FitzHugh-Nagumo neurobiological networks by only observing the membrane potential of each neuron, where the neurons are coupled linearly and nonlinearly, respectively. Comparison with the traditional Granger causality test is performed, and it is found that the new approach outperforms the traditional one. The impact of the network coupling strength and the noise intensity, as well as the data length of each partition of the time series, is further analyzed in detail. Finally, an application to a network composed of coupled chaotic Rössler systems is provided for further validation of the new method. © 2011 American Institute of Physics.

Citation Format(s)

Detecting the topologies of complex networks with stochastic perturbations. / Wu, Xiaoqun; Zhou, Changsong; Chen, Guanrong et al.
In: Chaos, Vol. 21, No. 4, 43129, 12.2011.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review