Bibliographic Details
| Title: |
Topological Characterization of Complex Systems: Using Persistent Entropy. |
| Authors: |
Merelli, Emanuela1 emanuela.merelli@unicam.it, Rucco, Matteo1 ruccomatteo@gmail.com, Sloot, Peter2,3,4 p.m.a.sloot@uva.nl, Tesei, Luca1 luca.tesei@unicam.it |
| Source: |
Entropy. Oct2015, Vol. 17 Issue 10, p6872-6892. 21p. |
| Subject Terms: |
*TOPOLOGY, *DATA analysis, *ENTROPY, *IMMUNE system, *IDIOTYPIC networks |
| 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. [ABSTRACT FROM AUTHOR] |
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| Database: |
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