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 categories 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.