Superfamily phenomena and motifs of networks induced from time series
Superfamily phenomena and motifs of networks induced from time series
复制标题
DOI：
10.1073/pnas.0806082105
复制
发表时间：
2008-12-16
影响因子：
11.1
通讯作者：
Small, Michael
中科院分区：
文献类型：
--
作者：
Xu, Xiaoke;Zhang, Jie;Small, Michael
我们介绍了从时间序列到复杂网络的转换，然后研究该网络中不同子图的相对频率。子图的分布可以用来区分和表征不同类型的连续动力学：周期，混沌和周期噪声。此外，虽然一般类型的动力学产生属于同一超家族的网络，特定的动力系统产生特征动力学。当应用于离散（地图）数据时，该技术区分混沌映射，超混沌映射和噪声数据。
We introduce a transformation from time series to complex networks and then study the relative frequency of different subgraphs within that network. The distribution of subgraphs can be used to distinguish between and to characterize different types of continuous dynamics: periodic, chaotic, and periodic with noise. Moreover, although the general types of dynamics generate networks belonging to the same superfamily of networks, specific dynamical systems generate characteristic dynamics. When applied to discrete (map-like) data this technique distinguishes chaotic maps, hyperchaotic maps, and noise data.