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.

Library of Congress Subject Headings

System analysis; Mathematical models; Equations--Construction

Publication Date


Document Type


Student Type


Department, Program, or Center

School of Mathematical Sciences (COS)


Nishant Malik

Advisor/Committee Member

Mary Lynn Reed

Advisor/Committee Member

Matthew Hoffman


RIT – Main Campus

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