Abstract

The framework of coevolving networks is a tool to model large-scale interacting dynamical systems undergoing change in connective structure, describing phenomena such as epidemic spreading on social networks with individuals changing their connections to avoid infection. Along with direct computational simulation, coevolving network systems are often formulated in terms of systems of ordinary differential equations in their descriptive statistics. The equation approach reduces the computational burden of analyzing the systems, but deriving equations becomes difficult as the underlying model becomes more complicated and as the desire for accuracy increases. We present an approach to construct equations for coevolving network systems automatically, using data from computational simulations and a formulation of sparse model identification. Using this approach we construct a data-driven system of equations for a coevolving SIS model that reproduces system behavior in both temporal evolution and dependence of steady states on system parameters.

Publication Date

7-31-2022

Document Type

Thesis

Student Type

Graduate

Department, Program, or Center

School of Mathematical Sciences (COS)

Advisor

Nishant Malik

Advisor/Committee Member

Mary Lynn Reed

Advisor/Committee Member

Matthew Hoffman

Campus

RIT – Main Campus

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