Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.3390/e17106872 | ENTROPY |
Keywords | Field | DocType |
topological data analysis,persistent entropy automaton,higher dimensional automata,immune system,idiotypic network,computational agents | Complex system,Topological data analysis,Automaton,Algorithm,Mathematics,Quantitative Concept | Journal |
Volume | Issue | Citations |
17 | 10 | 9 |
PageRank | References | Authors |
0.64 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emanuela Merelli | 1 | 498 | 54.79 |
| Rucco Matteo | 2 | 21 | 4.44 |
| Peter M. A. Sloot | 3 | 3095 | 513.51 |
| Luca Tesei | 4 | 177 | 22.01 |