New Dynamic Dependency Framework May Lead To Better Neural, Social And Tech Systems Models (IMAGE)

Bar-Ilan University

New Dynamic Dependency Framework May Lead To Better Neural, Social And Tech Systems Models

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In a paper published recently in Nature Physics, Bar-Ilan University Prof. Havlin, and a team of researchers, including Stefano Boccaletti, Ivan Bonamassa, and Michael M. Danziger, present a dynamic dependency framework that can capture interdependent and competitive interactions between dynamic systems which are used to study synchronization and spreading processes in multilayer networks with interacting layers. Main results in this image. (Top Left) Phase diagram for two partially competitive Kuramoto models with regions of multistability. (Top Right) Theoretical and numerical results for the ow in interdependent SIS epidemics (Erdos-Renyi graphs, average degree = 12). (Bottom Left) Path-dependent (awakening) transitions in asymmetrically coupled SIS dynamics. (Bottom Right) Critical scaling of bottlenecks (ghosts in saddle-node bifurcations) above the hybrid transitions in interdependent dynamics

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Prof. Shlomo Havlin and team

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