The study of network motifs induced from discrete time series
离散时间序列的网络模体分析

Complex network theory is used to characterize the temporal and phase space features of a time series when it is transferred into a network. In this paper, we study the motif ranks of complex networks induced from different cat egories of time series with periodic bifurcations and chaos, which are generated with two algorithms: the Visiblity Graph (VG) algorithm and the Phase-space Reconstruction (PR) algorithm. The advantages of both algorithms are analyzed.