Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/83654

Memory and Betweenness Preference in Temporal Networks Induced from Time Series

Authors Weng, Tongfeng HKUST affiliated (currently or previously).
Zhang, Jie
Small, Michael
Zheng, Rui HKUST affiliated (currently or previously)
Hui, Pan View this author's profile
Issue Date 2017
Source Scientific Reports , v. 7, February 2017, article number 41951
Summary We construct temporal networks from time series via unfolding the temporal information into an additional topological dimension of the networks. Thus, we are able to introduce memory entropy analysis to unravel the memory effect within the considered signal. We find distinct patterns in the entropy growth rate of the aggregate network at different memory scales for time series with different dynamics ranging from white noise, 1/f noise, autoregressive process, periodic to chaotic dynamics. Interestingly, for a chaotic time series, an exponential scaling emerges in the memory entropy analysis. We demonstrate that the memory exponent can successfully characterize bifurcation phenomenon, and differentiate the human cardiac system in healthy and pathological states. Moreover, we show that the betweenness preference analysis of these temporal networks can further characterize dynamical systems and separate distinct electrocardiogram recordings. Our work explores the memory effect and betweenness preference in temporal networks constructed from time series data, providing a new perspective to understand the underlying dynamical systems. © 2017 The Author(s).
ISSN 2045-2322
Language English
Format Article
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