Coevolution of information processing and topology in hierarchical adaptive random Boolean networks
Piotr Górski , Agnieszka Czaplicka , Janusz Hołyst
AbstractRandom Boolean Networks (RBNs) are frequently used for modeling complex systems driven by information processing, e.g. for gene regulatory networks (GRNs). Here we propose a hierarchical adaptive random Boolean Network (HARBN) as a system consisting of distinct adaptive RBNs (ARBNs) – subnetworks – connected by a set of permanent interlinks. We investigate mean node information, mean edge information as well as mean node degree. Information measures and internal subnetworks topology of HARBN coevolve and reach steady-states that are specific for a given network structure. The main natural feature of ARBNs, i.e. their adaptability, is preserved in HARBNs and they evolve towards critical configurations which is documented by power law distributions of network attractor lengths. The mean information processed by a single node or a single link increases with the number of interlinks added to the system. The mean length of network attractors and the mean steady-state connectivity possess minima for certain specific values of the quotient between the density of interlinks and the density of all links in networks. It means that the modular network displays extremal values of its observables when subnetworks are connected with a density a few times lower than a mean density of all links.
|Corporate author||The Faculty of Physics, WUT (WF)|
|Journal series||European Physical Journal B, ISSN 1434-6028|
|Publication size in sheets||1.65|
|Keywords in English||Statistical and Nonlinear Physics|
|Project||Self-Organised information PrOcessing, CriticaLity and Emergence in multilevel Systems . Project leader: Hołyst Janusz,
, Phone: 22 234 7133, start date 01-12-2012, end date 30-11-2015, 317534, Completed
|Score|| = 20.0, 18-05-2020, ArticleFromJournal|
= 25.0, 18-05-2020, ArticleFromJournal
|Publication indicators||= 1; = 8.0; = 4; : 2016 = 0.622; : 2016 = 1.461 (2) - 2016=1.368 (5)|
|Citation count*||8 (2020-09-01)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.