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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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.
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.
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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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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.