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

H. Sayama
IEEE Symposium on Artificial Life, ALIFE 07, 214-221, 2007.

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.

This paper in ALIFE

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