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Topological Characterization of Complex Systems: Using Persistent Entropy

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School of Science and Technology, University of Camerino, Camerino 62032, Italy
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Computational Science, University of Amsterdam, Amsterdam 1098 XH, The Netherlands
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Complexity Institute, Nanyang Technological University, Singapore 637723, Singapore
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ITMO University, St. Petersburg 199034, Russian
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Author to whom correspondence should be addressed.
Academic Editor: Raúl Alcaraz Martínez
Entropy 2015, 17(10), 6872-6892; https://doi.org/10.3390/e17106872
Received: 28 July 2015 / Revised: 17 September 2015 / Accepted: 29 September 2015 / Published: 15 October 2015
(This article belongs to the Section Information Theory, Probability and Statistics)
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 the S[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. View Full-Text
Keywords: topological data analysis; persistent entropy automaton; higher dimensional automata; immune system; idiotypic network; computational agents topological data analysis; persistent entropy automaton; higher dimensional automata; immune system; idiotypic network; computational agents
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Merelli, E.; Rucco, M.; Sloot, P.; Tesei, L. Topological Characterization of Complex Systems: Using Persistent Entropy. Entropy 2015, 17, 6872-6892.

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