A co-evolving process of colored vertices and state-dependent edges in dense random graphs converges to a limiting Markov process on colored graphons, with Fisher-Wright diffusion for color densities and a color-dependent stochastic flow for edges.
The voter model on random regular graphs with random rewiring
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Characterizes the distributional mean-field limit of co-evolving latent space networks with feedback, including empirical measures and graphon convergence, via a conditional propagation of chaos result.
The joint mixing time of the random walk and dynamical random-cluster process is Θ(log n) when edge updates are fast enough in the subcritical regime on random regular graphs.
Centered and scaled subgraph count vectors in the voter model on dynamic random graphs converge to a multidimensional Gaussian process as the number of vertices tends to infinity.
citing papers explorer
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Co-evolving vertex and edge dynamics in dense graphs
A co-evolving process of colored vertices and state-dependent edges in dense random graphs converges to a limiting Markov process on colored graphons, with Fisher-Wright diffusion for color densities and a color-dependent stochastic flow for edges.
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Mean-Field Analysis of Latent Variable Process Models on Dynamically Evolving Graphs with Feedback Effects
Characterizes the distributional mean-field limit of co-evolving latent space networks with feedback, including empirical measures and graphon convergence, via a conditional propagation of chaos result.
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Logarithmic Mixing of Random Walks on Dynamical Random Cluster Models
The joint mixing time of the random walk and dynamical random-cluster process is Θ(log n) when edge updates are fast enough in the subcritical regime on random regular graphs.
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Functional central limit theorem for the subgraph count of the voter model on dynamic random graphs
Centered and scaled subgraph count vectors in the voter model on dynamic random graphs converge to a multidimensional Gaussian process as the number of vertices tends to infinity.