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Entropy 2015, 17(10), 6872-6892; doi:10.3390/e17106872

Topological Characterization of Complex Systems: Using Persistent Entropy

1
School of Science and Technology, University of Camerino, Camerino 62032, Italy
2
Computational Science, University of Amsterdam, Amsterdam 1098 XH, The Netherlands
3
Complexity Institute, Nanyang Technological University, Singapore 637723, Singapore
4
ITMO University, St. Petersburg 199034, Russian
*
Author to whom correspondence should be addressed.
Academic Editor: Raúl Alcaraz Martínez
Received: 28 July 2015 / Revised: 17 September 2015 / Accepted: 29 September 2015 / Published: 15 October 2015
(This article belongs to the Section Information Theory)
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Abstract

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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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

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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