Topological Characterization of Complex Systems: Using Persistent Entropy
Topological Characterization of Complex Systems: Using Persistent Entropy
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DOI：
10.3390/e17106872
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发表时间：
2015-10-01
期刊：
影响因子：
2.7
通讯作者：
Tesei, Luca
中科院分区：
文献类型：
--
作者：
Merelli, Emanuela;Rucco, Matteo;Tesei, Luca
在本文中，我们提出了一种利用从拓扑数据分析中提取的信息来推导复杂系统模型的方法。我们方法的核心是用两级模型表示复杂系统的[B]范例。其中一个能级，结构S能级，是利用新引入的持久熵的量化概念导出的，并用持久熵自动机来描述。另一层是行为B层，其特征是由相互作用的计算主体组成的网络。提出的方法被应用于一个真实的案例研究--哺乳动物免疫系统的独特型网络。
In this paper, we propose a methodology for deriving a model of a complex system by exploiting the information extracted from topological data analysis. Central to our approach is theS [ B ]paradigm in which a complex system is represented by a two-level model. One level, the structural S one, is derived using the newly-introduced quantitative concept of persistent entropy, and it is described by a persistent entropy automaton. The other level, the behavioral B one, is characterized by a network of interacting computational agents. The presented methodology is applied to a real case study, the idiotypic network of the mammalian immune system.