Mapping to Complex Networks from Chaos Time Series in the Car Following Model

A new method to construct complex networks from chaos time series is proposed, which each phase space represented by a single node in the network. The method can be used to understand the dynamics of chaos time series (CTS) from the complex network's perspective. We find that different threshold values of status space distance correspond to different network topologies including star, scale-free, single-scale and broad-scale networks. Another finding is that, with the increase of threshold value, the CTS generate networks that exhibit small world features. Compared with pseudoperiodic time series, the chaos attractor may reveal a more heterogeneous structure. Significantly, We develop a unified evolving mechanism to generating different complex network by chaos prediction and adjusting threshold value. It is found that the chaos attractor is essential for the emergence of a common scale-free, single-scale and broad-scale network structures.