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.
Department, Program, or Center
School of Mathematical Sciences (COS)
Mary Lynn Reed
John, Nicholas, "Data-Driven Equations for Coevolving Network Systems" (2022). Thesis. Rochester Institute of Technology. Accessed from
RIT – Main Campus