Departament de Fisica, Universitat Politecnica de Catalunya, Colom 11, ES-08222 Terrassa, Barcelona, Spain.
Sci Rep. 2016 Jul 11;6:29804. doi: 10.1038/srep29804.
Many natural systems can be represented by complex networks of dynamical units with modular structure in the form of communities of densely interconnected nodes. Unraveling this community structure from observed data requires the development of appropriate tools, particularly when the nodes are embedded in a regular space grid and the datasets are short and noisy. Here we propose two methods to identify communities, and validate them with the analysis of climate datasets recorded at a regular grid of geographical locations covering the Earth surface. By identifying mutual lags among time-series recorded at different grid points, and by applying symbolic time-series analysis, we are able to extract meaningful regional communities, which can be interpreted in terms of large-scale climate phenomena. The methods proposed here are valuable tools for the study of other systems represented by networks of dynamical units, allowing the identification of communities, through time-series analysis of the observed output signals.
许多自然系统可以用复杂的网络来表示，这些网络中的动力学单元具有模块化结构，表现为节点之间密集连接的社区形式。要从观测数据中揭示这种社区结构，需要开发合适的工具，特别是当节点嵌入在规则的空间网格中且数据集较短且存在噪声时。在这里，我们提出了两种识别社区的方法，并通过对覆盖地球表面的规则地理位置网格上记录的气候数据集的分析来验证它们。通过识别不同网格点记录的时间序列之间的相互滞后，以及应用符号时间序列分析，我们能够提取出有意义的区域社区，可以用大规模气候现象来解释。这里提出的方法是研究由动力学单元网络表示的其他系统的有用工具，可以通过对观测输出信号的时间序列分析来识别社区。
