Generative network automata: A generalized framework for modeling complex dynamical systems with autonomously varying topologies

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)

Abstract

We propose a new modeling framework "Generative Network Automata (GNA)" that can uniformly describe both state transitions and autonomous topology transformations of complex dynamical networks. GNA is formulated as an extension of existing complex dynamical network models to include a new set of generative update rules that determine how local network topologies will change based on the current local network states and topologies. This paper introduces basic concepts of GNA, its formal definitions, its generality to represent other dynamical systems models, and some preliminary results of an exhaustive sweep of possible dynamics found in elementary binary GNA with restricted updating rules.

Original languageEnglish
Title of host publicationProceedings of the 2007 IEEE Symposium on Artificial Life, CI-ALife 2007
Pages214-221
Number of pages8
DOIs
Publication statusPublished - 2007 Sep 25
Externally publishedYes
Event1st IEEE Symposium on Artificial Life, IEEE-ALife'07 - Honolulu, HI, United States
Duration: 2007 Apr 12007 Apr 5

Publication series

NameProceedings of the 2007 IEEE Symposium on Artificial Life, CI-ALife 2007

Other

Other1st IEEE Symposium on Artificial Life, IEEE-ALife'07
CountryUnited States
CityHonolulu, HI
Period07/4/107/4/5

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Biochemistry, Genetics and Molecular Biology(all)

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  • Cite this

    Sayama, H. (2007). Generative network automata: A generalized framework for modeling complex dynamical systems with autonomously varying topologies. In Proceedings of the 2007 IEEE Symposium on Artificial Life, CI-ALife 2007 (pp. 214-221). [4218889] (Proceedings of the 2007 IEEE Symposium on Artificial Life, CI-ALife 2007). https://doi.org/10.1109/ALIFE.2007.367799