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Temporal Complex Network Analysis

  • Zhongke GaoEmail author
  • Yuxuan Yang
  • Qing Cai
Chapter

Abstract

Characterizing dynamical processes in a time-dependent complex system from observed time series is of great significance in many fields. Traditional time series analysis methods have difficulty in coping with some specific burdens, resulted from the increase of complexity of systems. Complex network, emerged in the last decade, provided a solution for dealing with these burdens. In this chapter, we introduce the basic concepts of complex network analysis of time series and some typical methods from univariate time series, namely, recurrence network, visibility graph, and horizontal visibility graph methods. In addition, the complex network analysis of multivariate time series is still a hot topic especially in the nowadays Big Data time. In this chapter, we still provide a case of multiscale complex network from multivariate time series and its application. Lastly, we introduce an expository example of a complex network-based study, to reveal the research steps in a complex network analysis for multivariate EEG signals where two different complex network methods are given.

Keywords

Complex network Time series Recurrence network Visibility graph EEG signals 

Supplementary material

462234_1_En_14_MOESM1_ESM.zip (1.8 mb)
Code (ZIP 1817 kb)

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Electrical and Information EngineeringTianjin UniversityTianjinChina

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