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Multiscale dynamics of an adaptive catalytic network. (English) Zbl 1418.37086

Summary: We study the multiscale structure of the Jain-Krishna adaptive network model. This model describes the co-evolution of a set of continuous-time autocatalytic ordinary differential equations and its underlying discrete-time graph structure. The graph dynamics is governed by deletion of vertices with asymptotically weak concentrations of prevalence and then re-insertion of vertices with new random connections. In this work, we prove several results about convergence of the continuous-time dynamics to equilibrium points. Furthermore, we motivate via formal asymptotic calculations several conjectures regarding the discrete-time graph updates. In summary, our results clearly show that there are several time scales in the problem depending upon system parameters, and that analysis can be carried out in certain singular limits. This shows that for the Jain-Krishna model, and potentially many other adaptive network models, a mixture of deterministic and/or stochastic multiscale methods is a good approach to work towards a rigorous mathematical analysis.

MSC:

37H10 Generation, random and stochastic difference and differential equations
37C10 Dynamics induced by flows and semiflows
05C82 Small world graphs, complex networks (graph-theoretic aspects)
92B20 Neural networks for/in biological studies, artificial life and related topics
34E13 Multiple scale methods for ordinary differential equations
05C80 Random graphs (graph-theoretic aspects)
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