Physically-interpretable classification of biological network dynamics for complex collective motions
Physically-interpretable classification of biological network dynamics for complex collective motions
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DOI：
10.1038/s41598-020-58064-w
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发表时间：
2020-02-20
影响因子：
4.6
通讯作者：
Kawahara, Yoshinobu
中科院分区：
文献类型：
--
作者：
Fujii, Keisuke;Takeishi, Naoya;Kawahara, Yoshinobu
理解生物网络动力学是各个科学和工程领域的基本问题。网络理论能够揭示元素之间的关系及其传播，但对于复杂的集体运动，网络的性质往往是瞬时的和复杂的变化。这里解决的一个基本问题是基于物理可解释的动力学特性对集体运动网络进行分类。在这里，我们应用一个数据驱动的频谱分析称为图形动态模式分解，获得集体运动分类的动力学特性。以一场球赛为例，我们对不同全局行为中的策略集体运动进行了分类，发现除了物理属性外，上下文节点信息对分类至关重要。此外，我们发现了标签特定的较强的光谱之间的关系最近的代理，提供物理和语义解释。我们的方法有助于从非线性动力系统的角度理解生物复杂网络动力学的原理。
Understanding biological network dynamics is a fundamental issue in various scientific and engineering fields. Network theory is capable of revealing the relationship between elements and their propagation; however, for complex collective motions, the network properties often transiently and complexly change. A fundamental question addressed here pertains to the classification of collective motion network based on physically-interpretable dynamical properties. Here we apply a data-driven spectral analysis called graph dynamic mode decomposition, which obtains the dynamical properties for collective motion classification. Using a ballgame as an example, we classified the strategic collective motions in different global behaviours and discovered that, in addition to the physical properties, the contextual node information was critical for classification. Furthermore, we discovered the label-specific stronger spectra in the relationship among the nearest agents, providing physical and semantic interpretations. Our approach contributes to the understanding of principles of biological complex network dynamics from the perspective of nonlinear dynamical systems.