Bibliographic Details
| Title: |
Topological characterization of S[B] systems: From data to models of complexity |
| Authors: |
Merelli, Emanuela, Rucco, Matteo, Sloot, Peter, Tesei, Luca |
| Publication Year: |
2015 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Emerging Technologies |
| More Details: |
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. The other level, the behavioral B one, is characterized by a network of interacting computational agents described by a Higher Dimensional Automaton. The methodology yields also a representation of the evolution of the derived two-level model as a Persistent Entropy Automaton. The presented methodology is applied to a real case study, the Idiotypic Network of the mammal immune system.
Comment: 26 pages, 10 figures |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/1505.05768 |
| Accession Number: |
edsarx.1505.05768 |
| Database: |
arXiv |
